Method and system for determining the position of a mobile device

ABSTRACT

Some embodiments use scanning devices to characterize radio signals received at a number of locations within a geographical area of interest. The signal characteristics along with the location information associated with the characteristics are stored in a centralized reference database. A mobile device characterizes signals it receives at a certain location and compares the characteristics with the signal characteristics stored in the reference database to obtain accurate location information of the certain location.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

The present application is a continuation-in-part of and claims thebenefit of U.S. patent application Ser. No. 12/059,558, entitled,“Methods and Systems for Determining the Location of an ElectronicDevice,” filed Mar. 31, 2008, published as U.S. Patent Publication2009-0243932. The present Application is also a continuation-in-part ofand claims the benefit of U.S. patent application Ser. No. 12/833,938,entitled, “Method and System for Determining the Position of a MobileStation,” filed Jul. 9, 2010. The present application also claims thebenefit of U.S. Provisional Patent Application 61/231,905, entitled,“Method and System for Determining the Position of a Mobile Station,”filed Aug. 6, 2009. The contents of U.S. Patent Publication2009-0243932, U.S. patent application Ser. No. 12/833,938, and U.S.Provisional Application 61/231,905 are hereby incorporated by reference.

BACKGROUND

With proliferation of mobile devices such as smart phones, netbooks andtablet computers, there is a growing desire to obtain accurate locationinformation of such devices because the information is of much use.Wireless positioning involves obtaining wireless signals and processingthe signals into a location estimate. Typical information used forpositioning includes Global Positioning System (GPS) signals, ReceivedSignal Strength Indicator (RSSI), Angle of Arrival (AOA), Time ofArrival (TOA), Time Difference of Arrival (TDOA), and Doppler shift.This information is often processed to find the position of a wirelessdevice. For example, triangulation is used where multiple range or anglemeasurements from known positions are used to calculate the position ofa device.

One of the sources of errors in wireless positioning is multipathpropagation. Multipath propagation occurs when a signal takes differentpaths when propagating from a source to a destination receiver. Whilethe signal is traveling, objects get in the way and cause the signal tobounce in different directions before getting to the receiver. As aresult, some of the signal will be delayed and travel longer paths tothe receiver. In other instances there is no direct line of sightbecause an object is completely blocking and any received signals occuronly due to multipath propagation. These effects cause errors in GPSdata, RSSI, AOA, TOA, TDOA and Doppler shift. The computed position ofthe device using common techniques such as triangulation will thereforebe incorrect.

Location-aware technologies compute the location of an object. Thesesystems differ in terms of accuracy, coverage, cost of installation, andmaintenance. GPS systems use satellite signals and work in outdoorenvironments. GPS systems, however, require direct line of sight and donot work in an indoor environment. Cell tower triangulation is anothermethod that uses signals from cellular towers to locate a wireless user.This method is also limited in accuracy and reliability because of thecoarse number of cell towers from a particular service provider that amobile user can communicate with, as well as multipath issues.

Systems have been developed in the past that use the strength ofwireless access point beacon signals in an outdoor environment tocalculate the position of a mobile user. One technique is to create adatabase of wireless beacons and use that information together with theamplitude of beacons signals received by a mobile device to compute thelocation of the mobile device. Other techniques use radio frequency (RF)wireless signal strength information and triangulation to locate objectsin an indoor environment. However, these methods provide poor indoorpositioning accuracy because RF signal amplitude is greatly affected bymetal objects, reflective surfaces, multipath, dead-spots, noise andinterference.

BRIEF SUMMARY

Some embodiments of the invention employ radio wave signals to calculatethe position of a wireless device in both indoor and outdoorenvironments. Initially a moving scanner (such as a vehicle equippedwith radios) scans a geographical area of interest and receives radiowaves from different sources that transmit radio waves in differentstandards such as GPS, Wireless Local Area Network (WLAN) 802.11,cellular (second generation (2G), third generation (3G), fourthgeneration (4G), etc.), Bluetooth®, Worldwide Interoperability forMicrowave Access (WiMAX), HD Radio™, Ultra-wideband (UWB) and 60 GHzstandards. The received signals are processed in order to characterizethem and the transmission channels at each position of the vehicle.

The position of the vehicle, identifiers of the sources of the signals,and characterizations of the received signals and the transmissionchannel are stored locally and then transferred to a networked databasein some embodiments. The information in the networked database thenserves as reference information for locating wireless devices.

In locating a wireless device, the device initially receives wirelesssignals. These signals are then characterized and compared to thereference database. Interpolation is then used in some embodiments tofind the position of the wireless device.

The preceding Summary is intended to serve as a brief introduction tosome embodiments of the invention. It is not meant to be an introductionor overview of all inventive subject matter disclosed in this document.The Detailed Description that follows and the Drawings that are referredto in the Detailed Description will further describe the embodimentsdescribed in the Summary as well as other embodiments. Accordingly, tounderstand all the embodiments described by this document, a full reviewof the Summary, Detailed Description and the Drawings is needed.Moreover, the claimed subject matters are not to be limited by theillustrative details in the Summary, Detailed Description and theDrawing, but rather are to be defined by the appended claims, becausethe claimed subject matters can be embodied in other specific formswithout departing from the spirit of the subject matters.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appendedclaims. However, for purpose of explanation, several embodiments of theinvention are set forth in the following figures.

FIG. 1 conceptually illustrates a mobile device in an environment withmultiple wireless networks.

FIG. 2 conceptually illustrates a radio receiver receiving a direct pathradio wave from a transmitter as well as two multipath waves.

FIG. 3 conceptually illustrates amplitude of a multipath channel profileof some embodiments.

FIG. 4 conceptually illustrates a moving scanner of some embodiments.

FIG. 5 conceptually illustrates examples of paths that a moving scannertakes in some embodiments.

FIG. 6 illustrates an exemplary channel characteristics table of someembodiments.

FIG. 7 conceptually illustrates a process that some embodiments use topopulate a reference database with signal characterization data.

FIG. 8 conceptually illustrates a mobile device of some embodiments.

FIG. 9 conceptually illustrates a process that performs interpolation orextrapolation of sample data stored in a database to calculate theposition of a mobile device in some embodiments.

FIG. 10 conceptually illustrates a process that some embodiments use tofind position of a mobile device.

FIG. 11 conceptually illustrates a process that some embodiments use tocalculate the position of a mobile device.

FIG. 12 conceptually illustrates the use of fixed radios instead ofmoving scanners in some embodiments.

FIG. 13 conceptually illustrates Multiple Input Multiple Output systemsof some embodiments.

FIG. 14 conceptually illustrates amplitude of a multichannel profile ofsome embodiments.

FIG. 15 illustrates the frame format in the 802.11a standard.

FIG. 16 illustrates the OFDM training structure in detail.

FIG. 17 conceptually illustrates a system of some embodiments that usesthe known transmission pattern of the training structure to determinethe time delay between a transmitter base station and a mobile receiver.

FIG. 18 conceptually illustrates magnitude output of a long preamblecorrelator of some embodiments.

FIG. 19 conceptually illustrates a system of some embodiments that usesthe Received Signal Strength Indication to determine the time delaybetween a transmitter base station and a mobile receiver.

FIG. 20 illustrates an example of a Received Signal Strength Indication(RSSI) signal output of some embodiments.

FIG. 21 illustrates a computer system with which some embodiments of theinvention are implemented.

DETAILED DESCRIPTION

In the following detailed description of the invention, numerousdetails, examples, and embodiments of the invention are set forth anddescribed. However, it will be clear and apparent to one skilled in theart that the invention is not limited to the embodiments set forth andthat the invention may be practiced without some of the specific detailsand examples discussed.

Some embodiments of the invention use wireless positioning methods thatcan work in both outdoor and indoor environments and can handlemultipath propagation and indirect line of sight. Some of the methods donot use triangulation so that positioning calculation is not affected byerrors caused by multipath propagation. These methods use measuredchannel characteristics which take multipath propagation into account,thereby overcoming the limitations of the existing technologies andsystems. These methods also use existing wireless infrastructure andstandards, thereby making the approach taken by these methods low-cost.

Some embodiments of the invention employ radio wave signals to calculatethe position of a wireless device in both indoor and outdoorenvironments. Initially a moving scanner equipped with radios scans ageographical area of interest and receives radio waves from differentsources that transmit radio waves in different standards such as GPS,WLAN 802.11, cellular (2G, 3G, 4G), Bluetooth®, WiMAX, HD Radio™, UWB,and 60 GHz standards. A moving scanner is a moving object that isequipped with one or more radios and can move about a geographical areaof interest. For example, a moving scanner in some embodiments is avehicle (e.g., a regular passenger car or a commercial truck) equippedwith radios to receive signals and can move to locations within thegeographical area of interest. In indoor environment, a moving scannercan be a small vehicle or a push-cart equipped with one or more radios.A moving scanner can also be a mobile device that is equipped with radioto receive radio signals at different locations to which the mobiledevice is carried by a user using the device. One of ordinary skill inthe art would realize that other moving objects equipped with radios canbe used without deviating from the teachings of the invention.

The received signals are processed in order to characterize them and thetransmission channels at each position of the moving object. Theposition of the moving object, identifiers of the sources of thesignals, and characterizations of the received signals and thetransmission channel are stored locally and then transferred to anetworked database in some embodiments. The information in the networkeddatabase then serves as reference information for locating wirelessdevices. In locating a wireless device, the device initially receiveswireless signals. These signals are then characterized and compared tothe reference database. Interpolation or extrapolation is then used insome embodiments to find the position of the wireless device.

A system that employs a known Radio Frequency (RF) transmission patternto locate a receiver object is described in the above mentioned U.S.patent application Ser. No. 12/833,938. As the signal travels between atransmitter and a receiver, the system calculates the time delay betweenthe two. By repeating this process with different transmitters withknown coordinates, it is possible to process the time delay informationand calculate the unknown position coordinates of the receiver.

A system that employs multi-tone Orthogonal Frequency-DivisionMultiplexing (OFDM) signals for locating an object is described in theabove mentioned U.S. Patent Publication 2009-0243932. As an OFDM signaltravels between a transmitter and a receiver, the system calculates theresidual phase difference between pilot tone frequency pairs. Byrepeating this process with different OFDM transmitters (or receivers)with known coordinates, it is possible to process the residual phasedifferences to calculate the unknown position coordinates of an OFDMtransmitter (or receiver).

In U.S. patent application Ser. No. 11/940,219 entitled, “Systems andMethods of Assisted GPS,” filed Nov. 14, 2007, and published as U.S.Patent Publication 2009/0121927, it is described that wireless accesspoints send differential assistance data to GPS receivers that areintegrated into cellular chipsets and other chipsets. Errors caused bymultipath travel of the GPS signals are reduced by using differentialassistance data generated by reference access point receivers with fixedlocations. U.S. Patent Publication 2009/0121927 is incorporated hereinby reference.

A method to find the location of a Radio-Frequency Identification (RFID)tag from RF communication with one or more RFID readers at variouspositions is described in U.S. patent application Ser. No. 11/641,624,entitled “RFID Location Systems and Methods.” filed Dec. 18, 2006, andpublished as U.S. Publication No. 2008-0143482 A1. A method of using thephase information of OFDM signals to locate a mobile device is describedin the above mentioned U.S. Patent Publication 2009-0243932. How signalswith a known Radio Frequency (RF) transmission pattern traveling betweentransmitters and a mobile receiver can be used to measure time ofarrival information and processed using multi-lateration to locate themobile receiver is described in the above mentioned U.S. patentapplication Ser. No. 12/833,938.

Some embodiments of the invention measure the characteristics of thechannel at various locations and store them in a database, therebyproviding a robust method of handling multipath propagation. The channelcharacteristics measure the behavior of the channel in that environment.The channel can include direct paths and/or indirect paths. In someembodiments, a wireless mobile device that needs to calculate itsposition characterizes the channels it experiences and compares them tothe characterization data stored in the database. The wireless deviceexperiences the same channel multipath behavior as the multipathbehavior measured at the same (or a nearby) position/location and storedin the database. Some embodiments update the database for changes in theenvironment such as the location of obstacles, objects or buildings.

This approach is applicable to indoor and outdoor environments, and usesexisting wireless infrastructure and handsets, thereby making theapproach low-cost and available at a wide variety of locations. Themethod is complementary to GPS since it works in indoor locations,tunnels and urban canyons, where GPS signals may be weak and/ormultipath issues could be present. The method can be applied to existingmobile phones and complement the phone's GPS positioning system, therebymaking a hybrid positioning system in some embodiments where the phonesare aware of their locations both inside and outside buildings.

Some embodiments provide a method of determining a location of a mobiledevice. The method receives a first group of signals with knowntransmission patterns at the mobile device from a set of transmitters.The first group of signals includes at least one multipath signal. Themethod computes a first set of parameters from the first group ofsignals by using the known transmission pattern. The method receives,from a database, a second set of parameters that are computed from asecond group of signals. The second group of signals includes at leastone multipath signal. Each parameter is associated with a location wherea corresponding signal from the second plurality of signals wasreceived. The method compares the first set of parameters with thesecond set of parameters and determines the location of the mobiledevice based on the comparison.

Some embodiments provide a computer readable medium that stores acomputer program for determining a location of a mobile device. Thecomputer program is executable by at least one processor. The computerprogram includes several sets of instructions. The sets of instructionsinclude a set of instructions for receiving a first group of signals atthe mobile device from a transmitting device over a set of transmissionchannels. The transmitting device includes a group of antennas. Each ofthe signals is received from one of the antennas of a transmittingdevice. The sets of instructions include a set of instructions forcharacterizing the set of transmission channels to compute a firstplurality of parameters associated with the first plurality of signals.The sets of instructions include a set of instructions for retrieving,from a database, a second group of parameters that are associated with agroup of signals previously received from the transmitting device by aset of scanning devices. The sets of instructions include a set ofinstructions for correlating the first group of parameters with thesecond group of parameters. The sets of instructions include a set ofinstructions for determining the location of the mobile device based onthe correlation.

Several more detailed embodiments of the invention are described insections below. Before describing these embodiments further, Section Iprovides an overview of several terms and concepts used in someembodiments. Next, Section II discusses characterizing radio signals topopulate a reference database with the signal characteristics. SectionIII describes finding location of a device by characterizing receivedsignals and comparing the characteristics with those stored in thereference database. Next, Section IV describes several alternativeembodiments to populate a reference database with the signalcharacteristics. Section V then describes multiple antenna systems whichcan be used in an environment where there are no multipath phenomena.Next, section VI describes an example showing how to extract signal orchannel characteristics from WLAN 802.11 signals. Finally, section VIIprovides a description of a computer system with which some embodimentsof the invention are implemented.

I. Terms and Concepts

A. Inphase and Quadrature

A general wireless system includes a transmitter component and areceiver component. The transmitter first performs digital modulation ofdata into an analog baseband signal. Analog modulation then shifts thecenter frequency of the analog signal up to the radio carrier, and theantenna transmits the signal. The receiver's antenna receives the analogradio signal and uses the known carrier to demodulate the signal intothe analog baseband signal. The receiver's baseband then converts theanalog baseband signal into digital data. In a direct down-conversionreceiver the carrier frequency is directly converted to two basebandsignals, the in-phase signal I(t) and the quadrature phase signal, Q(t).

The modulated physical signal (the passband or RF signal) can berepresented by a complex value called “the equivalent baseband signal”:

Z(t)=I(t)+jQ(t)

where I(t) is the inphase signal, Q(t) is the quadrature phase signal,and j is the imaginary unit. The frequency spectrum of this signalincludes negative as well as positive frequencies.

The physical passband signal corresponds to the real portion of theabove formula:

I(t)cos(wt)−Q(t)sin(wt)=Re{Z(t)e ^(jwt)}

where W is the carrier angular frequency in radian per second. In an“equivalent baseband model” of a communication system, the modulatedsignal is replaced by a complex valued “equivalent baseband signal” withcarrier frequency of 0 hertz, and the RF channel is replaced by an“equivalent baseband channel model” where the frequency response istransferred to baseband frequencies. Thus, the down conversion procedurereplaces a real channel with a complex equivalent.

B. Convolution

Convolution is a mathematical operation on two functions that produces athird function that is typically viewed as a modified version of one ofthe original functions. In signal processing, convolution is the processof constructing the output of an electronic system for any arbitraryinput signal by using the impulse response of system.

C. Multipath

Multipaths occur when the signals from the transmitters are received ata receiver after the signals are reflected by objects such as buildingsaround the receiver. Multipaths specially happen in areas with manybuildings, such as city centers. As described in detail below, althoughmultipath propagation is considered as interference in other systems anddegrade their performance, different embodiments of the invention takeadvantage of multipath signals to determine position of mobile deviceswith higher accuracy.

D. Symbol

A symbol is a state or significant condition of a communication channelthat persists for a fixed period of time. A transmitter places symbolson the channel at a known symbol rate and the receiver detects thesequence of symbols and reconstructs the transmitted data. A significantcondition is one of several values of a signal parameter that is chosento represent information.

II. Populating Channel Characteristics Database

FIG. 1 conceptually illustrates a geographic area 100 that has multiplewireless networks and a mobile device 105 at coordinates (x,y,z) thathas one or more wireless transceivers (not shown) that can use suchnetworks for communication and positioning. For simplicity, what isreferred in this specification as a mobile device, a mobile station, ora mobile includes user equipments such as cellular telephones, personalcommunications service (PCS) telephones, wireless-enabled personaldigital assistants (PDAs), wireless modems, laptop computers, tabletcomputers, smart phones, handheld devices that include short rangeradios (such as IEEE 802.11 or Bluetooth® but do not have cellularreceivers), handheld devices that include short range radios (such asIEEE 802.11 or Bluetooth® but do not have either cellular or GPSreceivers), digital still/video camera, or other wireless devices thatare network capable. Each type of network can also have differentimplementations. For example, the WLAN 802.11 standard can include802.11b, 802.11a, 802.11g, and 802.11n. What is referred to as acellular receiver in this specification is a receivers that operates oncellular frequencies that are licensed by the government and have arange of approximately 1 to 100 miles. The cellular network can includeGlobal Mobile System (GSM), Code Division Multiple Access (CDMA),CDMA2000, Wideband CDMA (WCDMA), Time Division Multiple Access (TDMA),Frequency Division Multiple Access (FDMA), and other multiple accesstechniques.

The networks include GPS, cellular (e.g., 2G, 3G, 4G), WLAN 802.11,Bluetooth®, Radio-Frequency Identification (RFID), WiMAX, HD Radio™,UWB, ZigBee, and 60 GHz. Each type of network can also have differentimplementations. For instance, the WLAN 802.11 standard can include802.11b, 802.11a, 802.11g, and 802.11n. The cellular network can includeCode Division Multiple Access (CDMA), CDMA2000, Wideband CDMA (WCDMA),Time Division Multiple Access (TDMA), Frequency Division Multiple Access(FDMA), and other multiple access techniques. A mobile device cancommunicate wirelessly with one or more of these networks. Thecommunication can be downlink from the network, for example, satellites110 and 112, WLAN base-stations 115-117 (i.e., wireless access points115-117), cellular towers 120 and 122, etc., to the mobile device 105.The communication can also be in the uplink direction from the mobiledevice to the network. FIG. 1 shows the downlink direction.

Some of the radio waves in FIG. 1 travel directly from the transmitterto the receiver, as illustrated by lines 125, while other radio wavesare totally obstructed and do not reach the receiver, as illustrated byline 130. Yet, other radio waves encounter obstacles that causereflections and multipaths before they reach the receiver, asillustrated by lines 135-138 which are directed to mobile device 105from satellite 112, wireless access point 116, cell tower 120, and celltower 122 respectively as illustrated in FIG. 1. Multipath propagationcauses errors in GPS data, RSSI, AOA, TOA, TDOA and Doppler shift. Forexample, time delays such as TOA and TDOA represent the longer multipathdistance rather than the actual distance between the transmitter and thereceiver. The longer multipath propagations also result in smallersignal amplitude for indicators such as RSSI, as well as incorrectvalues for AOA and Doppler shifts. These errors cause incorrect mobiledevice position calculations for traditional triangulation techniques.

Some embodiments of the invention overcome the limitations caused bymultipath propagation since instead of triangulation each channel ischaracterized at various positions and the characterization data arestored in a reference database. As described further below, in locatinga wireless device, the device initially receives wireless signals. Thesesignals are then characterized and compared to the data stored in thereference database. Interpolation or extrapolation is then used in someembodiments to find the position of the wireless device.

The multipath problem is conceptually illustrated in FIG. 2. As shown, aradio transmitter 200, a radio receiver 205 placed at coordinates(x₀,y₀, z₀), and several buildings 225-240. In this example, thereceiver 205 receives a direct path radio wave 210, as well as twomultipath waves 215 and 220 caused by reflections from obstacles,buildings 225 and 230, respectively.

FIG. 3 conceptually illustrates the amplitude of the superposition ofthese waves 210, 215 and 220 at the receiver's baseband afterdown-conversion and processing. It should be noted that the transmittertypically uses pulse shaping to contain the spectrum of the transmittedsignal to a narrowband. The receiver also convolves with a pulse shape.These actions result in a delta function. Down-conversion is aconversion of a digitized real signal centered at an intermediatefrequency (IF) into a basebanded complex signal centered at zerofrequency. An 802.11 receiver characterizing its channel and measuringtime of arrival delays is described in detail in the above mentionedU.S. Patent Publication 2009-0243932. The accuracy of positioncalculation in some embodiments increases as the number of transmittersis increased. Although the method can be applied to any radio-basedpositioning system, widely available systems such as WLAN 802.11 areparticularly useful because of the large number of public hotspots orprivate WLAN access points that are available. This means that a WLANradio receiver can often find several nearby WLAN transmitters. Cellulartowers, on the other hand, are far fewer in numbers and are much furtherdistances apart. FIG. 3 shows the direct path peak occurring at time T₁and the peaks corresponding to multipaths 215 and 220 occurring at latertimes T₂ and T₃, respectively.

Multipaths that are caused by reflections from obstacles create virtualtransmitter access points. For instance, the peak occurring at time T₂that is caused by multipath 215 can be viewed as originating fromvirtual transmitter 265, where the position of virtual transmitter 265is reflection of transmitter 200 on the wall of building 225. Likewise,the peak occurring at time T₃ that is caused by multipath 220 can beviewed as originating from virtual transmitter 270, where the positionof virtual transmitter 270 is reflection of virtual transmitter 265 onthe wall of building 230. Therefore, the greater the number ofmultipaths the greater is the number of virtual transmitters whichtranslates into a higher accuracy for the positioning method of thisinvention. This is in sharp contrast to conventional methods, wheremultipath propagation degrades system performance.

The signal that is transmitted by a transmitter is an input signal thatgoes through a wireless channel. A receiver then receives the outputsignal of the channel. The received signal of the channel is convolutionof the input signal with the impulse response of the channel. In thefrequency domain this means that the fast Fourier transform (FFT) of thereceived signal is multiplication of the FFT of the input signal withthe FFT of the impulse response of the channel. Throughout thisspecification the terms channel impulse response, channel multipathprofile, channel profile, and channel characteristics are usedinterchangeably.

FIG. 3 illustrates the amplitude of the multipath profilecharacterization of this particular transmitter's channel at coordinate(x₀,y₀,z₀). This figure represents the absolute value (modulus of thereal and imaginary part) of transmitter's channel impulse response atlocation (x₀,y₀,z₀). Some embodiment use scanning receivers to gatherchannel characterization data (such as shown in FIG. 3 or parametersderived from the channel) at various positions and to store them in areference database. The channel characterization at a given position isthen used as a signature to identify that location and distinguish itfrom other locations, since for a given transmitter such as transmitter200 the shape of FIG. 3 is a function of coordinates (x₀,y₀,z₀). Forexample, the further away distance the point (x₀,y₀,z₀) is fromtransmitter 200 the longer are the arrival times of the direct pathwaves and subsequent multipath waves. A mobile device in someembodiments then characterizes the channels it experiences and usesreference database interpolation and matching to find its position. Forinstance, a mobile device that is at coordinate (x₀,y₀,z₀) willexperience the same scanned channel characteristics illustrated in FIG.3. Then, when searching through the reference database, thesecharacteristics will match the measurements taken by scanning receiversat location (x₀,y₀,z₀). Hence, the mobile device calculates (x₀,y₀,z₀)as its location. That is, the mobile device finds that (x₀,y₀,z₀) is itslocation. When the mobile is near (but not at) coordinate (x₀,y₀,z₀),i.e., there is no matching data in the database, interpolation andextrapolation methods will be used to find its location in someembodiments. Multipath propagation reduces the accuracy of conventionaltriangulation positioning methods by introducing timing and distanceerrors. However, with the disclosed method, multipath propagationincreases the positioning accuracy because it makes the channelcharacteristics signature at one location more unique anddistinguishable from another location.

FIG. 3 only shows the amplitude of the channel's multipath profile.However, the signal illustrated in FIG. 3 also has a corresponding phasecomponent because down-converted RF signals are complex. The fastFourier transform (FFT) of the amplitude and corresponding phase of thesignal results in the channel's FFT coefficients. The channel's FFTcoefficients are one of the parameters that are stored in the referencedatabase in some embodiments. Some embodiments also process theamplitude and corresponding phase of a signal to derive other channelparameters. For instance, some embodiments perform peak detection on theprofile illustrated in FIG. 3 and store the magnitude and correspondingphase of the peaks and the times at which the peaks occur (i.e. T₁, T₂and T₃ shown in FIG. 3).

There are many parameters that can be used to characterize acommunication channel. As FIG. 3 illustrates, some are direct channelparameters (i.e., parameters directly related to/extracted from thereceived signal) while others are indirect parameters that are derivedfrom the channel. An example of a direct channel parameter is the timeof arrival delay using preamble correlation since it is directlyextracted from FIG. 3. An example of an indirect channel parameter isthe receiver's equalizer filter coefficients. The parameters that can beused for characterizing communication channels include channel multipathprofile (e.g., a multipath profile shown in FIG. 3), channel FFTcoefficients (e.g., FFT of amplitude and phase of a multipath profile),equalizer filter coefficients, time of arrival delay using preamblecorrelation, time of arrival delay using RSSI transition, timedifference of arrival, beacon signal strength, angle of arrival, phaseof each pilot tone, amplitude of each pilot tone, and Doppler shift (formoving objects). An equalizer (also referred as rake receiver) uses thechannel coefficient estimates to do division and multiplication in theFFT domain to decode characterization data. Doppler shift can be used tocalculate the speed and direction of a moving object. This can in turnbe used to predict future positions and complement the positioncalculation method.

FIGS. 2 and 3 illustrate one transmitter of one type of radio. In someembodiments, the transmitter 200 shown in FIG. 2 is a WLAN 802.11 radiotransmitter. In these embodiments, FIG. 3 shows one characterization ofan 802.11 channel between transmitter 200 and receiver 205. The same802.11 channel can be characterized using one or more of the parametersdescribed above. Also, in an environment where there are 802.11 radiotransmitters other than transmitter 200, those 802.11 channels can becharacterized by repeating channel characterization illustrated in FIGS.2 and 3. Moreover, when there are other types of radio transmitters(e.g. GPS, cellular, Bluetooth®, WiMAX, HD Radio™, UWB, 60 GHz, etc.),channels associated with those transmitters can be characterizedsimilarly by repeating channel characterization illustrated in FIGS. 2and 3 for those channels too. Thus, in terms of populating a centralizedcharacterization database for a given position coordinate there are atleast three categories of information: (1) multiple (i.e., different)characterization parameters of the same radio channel (e.g., channelmultipath profile, channel FFT coefficients, equalizer filtercoefficients, time of arrival delay using preamble correlation, time ofarrival delay using RSSI transition, time difference of arrival, beaconsignal strength, angle of arrival, phase of each pilot tone, amplitudeof each pilot tone, Doppler shift, etc.), (2) multiple radios of thesame radio types (e.g., 802.11 transmitter A, 802.11 transmitter B,etc.), and (3) multiple radio types (e.g., GPS, 802.11, cellular,Bluetooth®, etc.). Using multiple radios improves the accuracy. Inaddition, using multiple radio types enables a mobile device to stilldetermine its location even when one type of radio is not available. Forinstance, a mobile device that is capable of communicating with cellularradio as well as a short range radio (such as 802.11 or Bluetooth®) candetermine its location in an indoor location where cellular coverage isnot available.

FIG. 4 illustrates a conceptual environment 405 for characterizing thevarious types of radio communication channels and storing them in areference database in some embodiments. Specifically, FIG. 4 shows anexemplary moving scanner 400 of some embodiments. A moving scanner is amoving object that is equipped with one or more radios and can moveabout a geographical area of interest. For instance, a moving scanner insome embodiments is a vehicle (e.g., a regular passenger car or acommercial truck) equipped with radios to receive signals and can moveto locations within the geographical area of interest. In indoorenvironments, a moving scanner can be a small vehicle or a push-cartequipped with one or more radios. A moving scanner can also be a mobiledevice that is equipped with radio to receive radio signals at differentlocations to which the mobile device is carried by a user using thedevice. In some embodiments, the moving scanners are portable wirelessdevices, consumer hand-held wireless devices, GPS-enabled media players,GPS-enabled IPods®, laptops, and the like. One of ordinary skill in theart would realize that other moving objects equipped with radios can beused without deviating from the teachings of the invention.

The moving scanner includes equipments for scanning the environment,characterizing the various types of radio communication channels thatare present, and storing them in a reference database. The movingscanner 400 stores channel characteristics in a reference database 410.In some embodiments, the moving scanner 400 is equipped with differenttypes of communications radios 415. These radios include radios of GPS,WLAN, cellular (2G, 3G, 4G), Bluetooth®, WiMAX, HD Radio™, UWB and 60GHz standards. In some embodiments, the moving scanner 400 moves arounddifferent locations in a geographical area of interest. Each radio 415includes an antenna 420, a transmit module 430, a receive module 432,and a digital baseband module 435. The radios 415 of the moving scanner400, as well as the radios (e.g., satellites 440-443, access points444-449, and cell towers 450 and 455) in the geographical area ofinterest (i.e., the environment 405) may be Multiple-InputMultiple-Output (MIMO) systems that have multiple antennas whichtransmit independently and hence improve the accuracy by providing morechannel characteristics data. The MIMO embodiments of the invention aredescribed in detail further below.

A GPS is a CDMA system that uses Pseudo Noise (PN) codes with embeddeddata that provide satellite locations and times. The moving scanner'sGPS radio is of commercial quality in some embodiments and is used tocharacterize the GPS communication channel. In some embodiments, the GPSradio also provides position values for all other radio communicationchannel readings. A controller module 450 manages the operations of allradios 415. For example, the controller 450 can instruct a radio 415 toscan for a particular signal and perform a channel measurement on aparticular frequency with a particular FFT size and measurement time.The baseband 435 of each radio 415 processes the received signals tocharacterize the communication channel of that radio. The controller 450of some embodiments then transfers the channel characteristics data inan appropriate format into local memory 460. The configuration of mostof the wireless networks does not change with time since the signalsources (cell towers, permanent wireless access points, etc) do notmove. However, some networks change with time because the signal sourcesmove (e.g. GPS satellites make complete orbits of the earth every 24hours). For those channels that exhibit a time varying dimension, thechannels are characterized at different time instances. A clock 480 isused to put a time stamp on the channel characteristics that are storedin the local memory 460. In some embodiments, the clock can be obtainedfrom cell towers, GPS satellite clocks, or other external sources.

In some embodiments, the channel characteristics data stored in memory460 is transferred to a centralized networked database 410 at regulartime intervals. In some embodiments, the transfer of data is carried outby a local server 465 that makes a wired or wireless connection (e.g.,using cellular, public hotspot WLAN, or other wireless method) to anetworked server 470 which after authentication transfers the data tothe centralized reference database 410. In some embodiments, thenetworked server 470 is also physically or logically part of an accesspoint 444-449. One of ordinary skill in the art would realize thatcomponents can be added to or removed from the moving scannerconceptually illustrated in FIG. 4 without deviating from the teachingsof the invention.

In some embodiments, the moving scanner 400 moves (e.g., is drivenaround) and scans geographical areas periodically and update thecentralized database. Whenever the moving scanner 400 takes a newmeasurement at coordinates and provides newest channel characteristicsat the coordinates, the networked server 470 compares the newly measuredchannel characteristics data with those already stored in the database410 and updates the database because channel characteristics may havechanged. Channel characteristics at a particular location may be changedwhen some previously existing transmitters that were transmitting radiosignals to the location are removed altogether or moved to a newlocation; new transmitters are added and transmit additional signals tothe location; new buildings are built and obstruct signal paths; and/orpreviously existing buildings are demolished and no longer block orreflect signals. For instance, when new measurement data shows that nodata is received from a previously existing transmitter with aparticular media access control address (MAC address or MAC ID), it canbe concluded that the transmitter may have moved. In such cases, thecharacterization data associated with that transmitter will be removedfrom the centralized database 410 in some embodiments. Alternatively,when a transmitter MAC ID is detected that is not in the database, theID and the characterization data associated with that new transmitterwill be added to the database.

In alternative embodiments, instead of having a moving scanner (e.g., ascanning vehicle) moving around to characterize the channels, fixedradios are used that are installed at different locations within theareas of interest. For instance, fixed GPS receivers or other types ofwireless RF radios that are networked are installed at known coordinateson posts and buildings in some embodiments. These fixed radioscharacterize radio channels and generate characteristics data, forexample GPS channel characteristics data (such as delay, phase, range tosatellite, their IDs, etc.) and transmit the data at different timeinstances to a network server that processes the data further beforestoring the time, channel characteristics and position coordinates inthe centralized reference database. Since pre-existing or newly addedlow-cost consumer radios can be used to characterize signals andgenerate the data, the cost of driving to scan and update the databaseis eliminated and the implementation cost is reduced. In someembodiments, the fixed radios are short range access points.

In other embodiments, the scanning illustrated in FIG. 4 and furtherbelow in reference with FIG. 5 is performed by position-enabled mobiledevices with communication radios (e.g., mobile or hand-held devicesmoved around by humans on sidewalks or inside malls). These mobiledevices can use GPS or other type of radios to calculate their ownpositions first. They can then characterize other radio communicationchannels that are present at their positions, and upload and store thecharacterization data in the reference database. These alternativeembodiments are described further below.

FIG. 5 conceptually illustrates exemplary paths taken by a movingscanner (e.g., a scanning vehicle or a hand-held device) to characterizethe communication channels at measurement points along the paths. FIG. 5illustrates that communication channels are characterized at certainpoints along the path of the moving scanner (not shown). The points 510marked with X's in FIG. 5 are where channel characteristics measurementsare performed. In some embodiments, the moving scanner takesmeasurements on each side of a road. In other embodiments, themeasurements could be carried out on only one side of the road. Whenthere are more than several lanes on the road, the measurements can becarried out on more lanes. Generally, more measurement points within ageographical area of interest translate into more accurate sampling ofthe area.

Since the channel characteristics are measured by the moving scanneronly at the locations marked with X's, finding accurate locationinformation of the locations in between those X's requires some form ofinterpolation (or extrapolation) and matching. That is, interpolationand matching are required because the channel characterization data ofthose in-between locations would not exactly match to the data measuredat locations marked with X's. The accuracy of the interpolated valuesand this positioning method is increased by having locations marked withX's closer together and having many multipaths received by the receiverat the X locations.

In some cases, some of the characterized X locations may not experiencemultipaths. Also, some locations may only experience one multipath(reflected path) and no direct path. In such situations, the measuredchannel profile will exhibit one peak, and that provides littleinformation for the proposed multipath-based method in some embodiments.Thus, the “effective” grid points are those grid points that producemulti-peak profiles. In an environment where multipath phenomena do notoccur, time-delay methods (such as that described in the abovementioned, U.S. patent application Ser. No. 12/833,938) can be used insome embodiments. It should be appreciated that the triangulation isused to generate or update data for storing in the database.Triangulation, however, is not used when location of a mobile device isdetermined. As described further below, the location of a mobile deviceis determined by characterizing the signals that the mobile devicereceives and by comparing them with the information that is alreadystored in the database. Alternatively, differential phase methods suchas the methods described in the above mentioned U.S. Patent Publication2009-0243932 can be used to generate data for storing in the database.The system and method disclosed in FIG. 5 also apply to indoorsituations, such as malls and office buildings, where smaller movingscanners, hand-held instruments, or fixed-location radios are used insome embodiments. The moving scanner's measurement points in more openspaces (outdoor or indoor) can resemble different patterns such as moredensely sampled rectangular grids or irregular grid sampling patterns.

FIG. 6 conceptually illustrates an exemplary table (or list) 600 storedin the centralized channel characteristics reference database 410 insome embodiments. Table 600 lists the real and imaginary coefficients ofa 64 point FFT of the WLAN 802.11 channels, where the receiveddemodulated in-phase (I) and quadrature (Q) signals are in the timedomain and the receiver performs FFT to take the complex signals backinto the frequency domain. For each channel reading, the position of amoving scanner and an identifier of the transmitter (e.g., MAC ID) arealso stored in the table 600. For example, at location (x₀,y₀,z₀) thescanner receives two signals, one with a MAC address ID₁ and anotherwith MAC address ID₂. Likewise, at location (x_(n),y_(n),z_(n)) thescanner receives three signals with MAC addresses ID₄, ID₅, ID₆,respectively.

In some embodiments, the channel FFT coefficients may also betransformed into the time domain and stored. Table 605 shows themagnitude, phase and time of arrival delay of extracted peaks of amultipath channel profile at a receiver's baseband such as 802.11baseband 435 illustrated in FIG. 4. Table 605 shows the amplitude ofeach channel's multipath profile and the corresponding phase informationthat represent the time representation of the channel. It is alsopossible to extract features of the channel's multipath profile such asthe magnitude, phase and time delay of extracted peaks at the receiver'sbaseband. Table 605 shows that at location (x₀,y₀,z₀) the signal withMAC address ID₁ has three peaks and the signal with MAC address ID₂ hasone peak as indicated by peak index numbers. These peaks are extractedand their amplitude, phase and time delay are stored in the table.Likewise, at location (x_(n),y_(n),z_(n)) the signals with MAC addressesID₅, ID₆ and ID₇ have one peak, one peak and two peaks, respectively.

As described above by reference to FIG. 3, multipath propagations createmultiple peaks of signal waves that can be considered to have beenoriginated from virtual transmitters. A greater number of multipathscreates a greater number of peaks, which in turn makes the channelcharacteristics more unique and distinguishable. Since in someembodiments of the invention the methods of calculating positions ofmobile devices rely on correlation and interpolation of channelcharacteristics, the accuracy of the calculation increases withmultipath propagation. Therefore, unlike conventional methods whichsuffer from the presence of multipath, the methods of some embodimentsof the invention use multipath propagation to its advantage.

In some embodiments, a reference database such as database 410 describedabove by reference to FIG. 4 contains tables for other WLAN channelcharacteristics information such as equalizer filter coefficients, timeof arrival delay using preamble correlation, time of arrival delay usingRSSI transition, beacon signal strength, angle of arrival, phase of eachpilot tone, amplitude of each pilot tone, etc. in addition to thecharacteristics stored in the tables illustrated in FIG. 6. Theseadditional tables in some embodiments also contain channelcharacteristics of other communication standards such as GPS, cellular(2G, 3G, 4G), Bluetooth®, WiMAX, HD Radio™, UWB and 60 GHz.

FIG. 7 conceptually illustrates a process 700 for generating thereferences database in some embodiments. In some embodiments, process700 starts when a scanning device is directed to start scanning ageographical area of interest. A user can direct the mobile device byentering inputs, such as clicking a mouse button or tapping atouchscreen to select a user interface (UI) item, and/or pressing keyson a keyboard, etc. In other embodiments, the process starts when othersystem or software initiates a scanning. The scanning device can be anyof the devices described above (such as a moving scanner, mobile device,hand-held device, or fixed-location radio receivers). As describedabove, each of these scanning devices in some embodiments is equippedwith one or more radios of multiple types to receive radio signals.

Process 700 initially receives (at 705) one or more radio signals fromone or more signal sources at a location within a geographical area ofinterest. The geographical area of interest is a predetermined areawhere scanning is to take place. In some embodiments, this area is anarea where a service provider intends to provide location findingservice to its customers carrying mobile devices. The signal sources insome embodiments include radio transmitters that the process receivessignals from using radio receivers. The radio transmitters include anysystem that transmits signals for example in GPS, WLAN 802.11, cellular(2G, 3G, 4G), Bluetooth®, WiMAX, HD Radio™, UWB, and 60 GHz standards.The process receives these signals from different type of radiotransmitters using receivers that uses the same signal standard. Therecan be multiple number of radio transmitters of the same type that theprocess can receive signals from. Also, there can be differentimplementations of the same radio type. For example, as described above,the WLAN 802.11 standard can include 802.11a, 802.11b, 802.11g, and802.11n.

Once the process receives all signals from all signals sources, theprocess identifies (at 710) the current location where the processreceived the signals. In the embodiments where the scanning device is amoving scanner or a hand-held device, the location of the scanningdevice is known because the scanning device receives and characterizessignals at predetermined locations such as the locations marked with X'sas described above by reference to FIG. 5.

Likewise, in those embodiments where an access points receives andcharacterize signals, the location information of the location where theaccess point is stationed is known. In other embodiments, the processidentifies location information using one of the radio receivers, forexample, a commercial quality GPS radio. In different embodiments, theprocess characterizes the signals it received and compares thecharacterization data with the data stored in a reference database tofind out its location information as described above. In someembodiments, the location information includes the coordinates of thelocation.

The process then proceeds to extract (at 715) characteristics of each ofthe signals received from the radio transmitters. The processcharacterizes all signals from different types of radio transmitters andmultiple radio transmitters of the same radio type. The process receivessignals in direct path or multipaths (indirect or reflected paths) asdescribed above. The process characterizes each signal using manydifferent parameters. The parameters that can be used for characterizingcommunication channels may include channel multipath profile (e.g., amultipath profile shown in FIG. 3), channel FFT coefficients (e.g., FFTof amplitude and phase of a multipath profile), equalizer filtercoefficients, time of arrival delay using preamble correlation, time ofarrival delay using RSSI transition, time difference of arrival, beaconsignal strength, angle of arrival, phase of each pilot tone, amplitudeof each pilot tone, and Doppler shift (for moving objects), as describedabove. It should be noted that Tables 600 and 605 list of few of theseparameters. A person or ordinary skill in the art would recognize thatother parameters extracted by the disclosed techniques can also bestored in these or similar tables and databases.

Next, the process associates (at 720) the identified locationinformation and the signal characteristics of the signals it received atthat location. The association can be a form of table such as table 605as described above by reference to FIG. 6, or in any type of dataassociation. As described above, the configuration of most of thewireless networks does not change with time since the signal sources(cell towers, permanent wireless access points, etc) do not move.However, some networks change with time because the signal sources move(e.g. GPS satellites make complete orbits of the earth every 24 hours).For those channels that exhibit a time varying dimension, process 700characterizes the channels at different time instances. The process usesa clock to put (at 725) a time stamp on the channel characteristics. Insome embodiments, the process obtains the network clock from celltowers, GPS satellite clocks, or other sources. The process then stores(at 730) these associated location information, signal characteristics,and time stamp (if applicable) in a reference database. The databasecould be locally accessible or remotely accessible via networks. Someexamples of a reference database are described above by reference toFIG. 4.

Next, the process determines (at 735) whether there is any otherlocation within the geographical area of interest at which tocharacterize signals. The process in some embodiments goes through alist of predetermined locations and moves to a next location at whichsignal characterization has not been performed. In other embodimentswhere a scanning device is stationed at a known location, the processwould not have another location to perform signal characterization at.In some embodiments, there are multiple numbers of scanning devicescovering the geographical area of interest and the process performssignal characterization at the location within some portion of thegeographical area of interest.

When the process determines that there are other locations within thegeographical area of interest at which to perform signalcharacterization, the process returns to 705 to receive signals at thenew location. Otherwise, the process determines that there are no morelocations to perform signal characterization. The process then ends. Thestored information (i.e., the associated location information and signalcharacteristics of signals) is used to determine the location of amobile device within the geographical area of interest for which theinformation is collected.

One of ordinary skill in the art will recognize that process 700 is aconceptual representation of the operations used to generate thereference database and to determine the location of a device. Thespecific operations of process 700 may not be performed in the exactorder shown and described. The specific operations may not be performedin one continuous series of operations, and different specificoperations may be performed in different embodiments. Furthermore, theprocess could be implemented using several sub-processes, or as part ofa larger macro process.

For instance, in some embodiments, process 700 is performed by one ormore software applications that are executing on one or more scanningdevices. In other embodiments, a second device (such as a networkedserver) which is communicatively coupled to the scanning device(s) candetermine the location of the scanning device at 710 (e.g., bytriangulation). Also, the scanning device in some embodiments sends thesignals that the scanning device receives to the second device (e.g., tothe networked server) which in turn performs operations 715-730. In someother embodiments, the scanning device performs (1) characteristicsextraction at 715, (2) characteristics extraction at 715 and associationat 720, or (3) characteristics extraction 715, association at 720, andtime stamping at 725 while the second device performs the rest of theoperations. In other words, any of the operations 710-7130 can either beperformed by a scanning device or by a second device that iscommunicatively coupled to the scanning device.

III. Obtaining Location Information

A. Finding Location of a Mobile Device

FIG. 8 conceptually illustrates a mobile device that characterizessignals it receives at a location in order to find its locationinformation. In some embodiments, mobile device 800 characterizes itscommunication channels, and a position module 805 matches the channelcharacteristics with the channel characteristics stored in a referencedatabase 825 to find the mobile device's position. The mobile device 800in some embodiments has one or more types of radios 810 similar to thoseradios described above by reference to FIG. 4. The mobile device 800uses the radios 810 to communicate and characterize the channels itexperiences. While the signal characteristics measured by the scanner400 illustrated in FIG. 4 are stored in a centralized database, thesignal characteristics of the mobile device's communication channelsmeasured by the mobile device 800 are compared to those stored in thecentralized reference database in order to find or calculate the mobiledevice's position or location information. In some embodiments, positionor location information of a mobile device includes the actualcoordinates of the mobile device. In some embodiments, the comparison ofthe characteristics is carried out by a position module 805. In someembodiments, a position module can reside on the mobile device 800. Inother embodiments, the position module is on a network server 815. Inanother embodiments, the position module may be on any other server thatthe mobile device 800 or the network server 815 can communicate with.

In some embodiments, the mobile device 800 is doing the calculations. Inthese embodiments, the mobile device's local server 820 makes aconnection, for example, using cellular, public hotspot WLAN or someother wireless method, to a networked server 815 and afterauthentication requests and downloads a subset of the data from thecentralized reference database 825. In some embodiments, there is noneed for the mobile device to download the entire reference databasesince the approximate location of the mobile device is known from priorposition calculations or from the beacon signals and cell tower signalsthe mobile device receives. The position module 805 on the mobile device800 then compares the mobile device's channel characteristics with thosedownloaded from the reference database 825 and uses interpolation (orextrapolation) and matching techniques to find the mobile device'sposition or location information.

In other embodiments, position calculation is performed on the networkserver. In these embodiments, there is no need for the mobile device 800to request and download characterization data from the referencedatabase 825. Instead the mobile device 810 in these embodimentstransfers its channel characteristics measurements to the network server815 and the server's position module 805 compares them to those in thereference database and uses interpolation and matching to find themobile device's position. In some embodiments, the position module 805is a part of or running on the network server 815. In other embodiments,the position module 805 resides on another server and communicates withthe network server 815. The server 815 then sends the mobile device'scalculated position back to the mobile device 800. A navigation module840 on the mobile device may also use the calculated position fornavigation purposes. One of ordinary skill in the art would realize thatcomponents can be added to or removed from the mobile device 800conceptually illustrated in FIG. 8 without deviating from the teachingsof the invention.

In some embodiments, the signal characterization data stored in acentralized database are corrected if the data contains inconsistenciesor are outdated. The measurements and calculated position of a mobiledevice may flag database inconsistencies to the networked server so thatthey are corrected. After the position module 805 on the mobile device800 or on server 815 has calculated the location of the mobile device805 it can compare the measured channel characteristics data with thosestored in the database and notify the networked server 815 aboutinconsistent data.

The inconsistencies can be caused when some previously existingtransmitters that were transmitting radio signals to the location of themobile device are removed, new transmitters are added and transmitadditional signals to the location, new buildings are built and obstructsignal paths, previous existing buildings are demolished and no longerblock or reflect signals. For example, the mobile device receivessignals from three access points and the mobile device's position iscalculated by the position module based on the signal characteristicsfrom those three access points. However, the database also has a fourthaccess point signal for scanned points that are close to the mobiledevice's position. This could indicate that the fourth access point isno longer functioning or has been moved from the location where thefourth access point used to be stationed. In some embodiment, thenetworked server 815 can then remove or initiate a removal of thecentralized database entries for the fourth access point, or send aservice request to a scanning device (e.g., moving scanner, mobiledevices, access points, etc.) to scan that area again in order to updatethe database 825.

B. Position Calculation

Some exemplary position calculation methods of the invention will now bedescribed. Suppose that a reference centralized database includessampled versions of the channels' multipath profile illustrated in FIG.3 denoted by d_(ij)(k). The index j represents a particular transmitter(for example, each unique MAC ID represents one value for j) and index irepresents each position characterized by a scanning device (e.g.,scanning vehicle, access points, mobile scanning device, etc.) asillustrated in FIG. 5 (the points marked by X) that are stored in adatabase as a table illustrated in FIG. 6. In this example, the variablek is a sampled version of time for the channel's multipath profile thatis stored in the database. Suppose also that the mobile deviceillustrated in FIG. 8 whose position is to be determined seestransmitter j and measures the transmitter's channel's multipathprofile. The profile is then denoted by the notation, d_(j)(k).

In order to find out the position of the mobile device, the profilesmeasured at the position of the mobile device are compared andcorrelated with the profiles which were measured by one or more scanningdevices at each measurement position and stored in the database. Thecorrelation can be performed by using the multipath channel profiles asshown in FIG. 3, or by using the extracted peak information of themultipath channel profiles as shown in table 605 illustrated in FIG. 6.The higher the correlation, the closer the mobile device is to thatparticular database position. That is, the higher the correlationbetween the profiles, the closer the mobile device is to that particularposition at which the profile stored in the database is measured. Thevalue of the correlations can be normalized and used as weights whencalculating the position of the mobile device. In this following simpleexample, it is assumed that clocks of the access points and the mobiledevice (as well as the scanning device's radios) are synchronized.However, the clock synchronization is not a requirement in someembodiments.

FIG. 9 conceptually illustrates a process 900 performed by a positionmodule such as position module 805 shown in FIG. 8. Specifically, FIG. 9conceptually illustrates a process that performs interpolation orextrapolation of sample data stored in a database to calculate theposition of a mobile device in some embodiments. Position calculationsin some embodiments are described further below by reference to FIG. 10and FIG. 11. In some embodiments, the position module can reside on themobile device. In other embodiments, the position module is on a networkserver. In different embodiments, the position module may reside on anyother server that the mobile device or the network server cancommunicate with.

In some embodiments, process 900 starts when the position module hascharacterized all signals received at a location via radio receivers ofa mobile device from all signal sources transmitting the signals to themobile device at the location. The position module has also retrievedcharacterization data stored in a reference database. In someembodiments when a channel exhibits a time varying dimension (e.g.,because the signal source is moving to different locations at certaintime such as an orbiting satellite), the channel characteristics storedin the database are characterized and time stamped at differentinstances. For signals from time varying sources, process 900 uses thetime stamp when doing correlation.

In some embodiments, process 900 initially correlates (at 905) thecharacteristics profile of the received signals with the profiles ordata retrieved from the reference database by calculating correlationcoefficient. The process calculates a correlation coefficient for theprofile of the received signals and each profile for the differentlocations stored in the database. An equation to calculate a correlationcoefficient in some embodiments is:

$C_{i} = {\sum\limits_{j}\; {\sum\limits_{k}\; {{d_{j}(k)}{d_{ij}(k)}}}}$

where C_(i) is the correlation coefficient for the profile measured by amobile device and the profile measured by a scanning device at aposition with coordinates (x_(i),y_(i),z_(i)) that has index i whenstored in the database. Non-coherent or coherent correlation can be usedhere. Non-coherent correlation uses just the amplitude of the multipathprofile, whereas coherent correlation uses both amplitude and phase ofthe multipath profile. A variation of the above equation can be made byadding a Gaussian multiplier centered on the extracted peaks of thereference database's channel measurements. The Gaussian can have astandard deviation that depends on the database grid sampling distanceand sampling frequency of the receiver. This improves accuracy in thepresence of sampling errors and measurement errors.

Next, the process normalizes (at 910) the calculated correlationcoefficients. The correlation coefficients can be normalized in someembodiments by dividing them with the sum of all correlationcoefficients (e.g., of all nearby locations in the database) as shown bythis equation:

$w_{i} = \frac{C_{i}}{\sum\limits_{i}C_{i}}$

where w_(i) is the normalized correlation coefficient and is also theweight for scanning device database position that has index i.

Next, the process calculates (at 315) the position of the mobile device(x, y, z) by calculating a weighted average value of the databasepositions. The weighted average value of the database positions(x_(i),y_(i),z_(i)) is calculated using these equations:

${x = {\sum\limits_{i}{w_{i}x_{i}}}};\mspace{14mu} {y = {\sum\limits_{i}{w_{i}y_{i}}}};\mspace{14mu} {z = {\sum\limits_{i}{w_{i}z_{i}}}}$

The weighted average of the database positions is the calculatedposition of the mobile device which used this interpolation technique.The process then ends.

One of ordinary skill in the art will recognize that process 900 is aconceptual representation of the operations performed to dointerpolation for position calculation. The specific operations ofprocess 900 may not be performed in the exact order shown and described.The specific operations may not be performed in one continuous series ofoperations, and different specific operations may be performed indifferent embodiments. Furthermore, the process could be implementedusing several sub-processes, or as part of a larger macro process (e.g.,process 1000 which is described further below by reference to FIG. 10)

In some embodiments, the process 900 performs the correlationcoefficient calculation and the weighted averages calculation operationsusing a limited number of positions stored in a database since the priorposition of the mobile device limits the search area for the currentposition. That is, from prior position of the mobile device, it can befigured out which of the positions and their characterization datastored in the database are sufficient for those calculations, instead ofusing all positions and data stored in the database.

In some embodiments, in such cases when a prior position of a mobiledevice is not available, an approximate area for the possible locationof the mobile device can be found from access point beacon signals andcell tower signals. That is, the profiles and signal characteristics ofthe locations within the approximate area can be found in the referencedatabase using the identifications of the access points and cell towersthat the mobile device received the beacon signals from.

An alternative correlation and interpolation operations that process 900performs in some other embodiments is to first find the set of gridpoints, such as the measurements points marked with X's as illustratedin FIG. 5, that are nearest to the position of the mobile device andthen to apply interpolating technique using measurements taken just atthose nearest points. For example, when the reference grid positionsstored in database make rectangular shapes, the correlation coefficientsfor the four corner X points of each rectangle are summed up and thenthe rectangle with the largest sum of the correlation coefficients ispicked. Then, the mobile device's position can be found by performing aweighted average value calculation as described above but this timeusing only the four corners of the picked rectangle. In someembodiments, the correlation and interpretation can also be applied tothe cases where grid positions stored in database make non-rectangularshapes.

In some cases, some of the reference grid locations do not producemultiple peak profiles and therefore they do not produce muchinformation for the proposed method and are not effective grid points.In some embodiments, the characterization data measured at thoselocations that do not have multiple peak profiles are not stored in thereference database in the first place. In other embodiments, such dataare stored in the reference database and then will be post-processed, beremoved from the database, or just be ignored during the interpolationand matching stage. In these embodiments, only the nearest multiple peakprofile grid points will be used. As a result, the shape of the“effective” grid in these embodiments can be irregular.

In some cases, the clock of the mobile device or the clocks of thescanning device's radio are not synchronized to the clock of the accesspoints transmitting radio signals. This means that even at measured gridpoints the received multipath channel profiles from the mobile devicecould be shifted by some offset as compared to those in the referencedatabase. That is, the multipath channel profiles measured by the mobiledevice at a location may be out of phase with the multipath channelprofiles measured by the scanning device at the same location becausethe clocks that are used to timestamp are not synched. It follows thatthe multipath channel profiles measured by the mobile device would lookshifted in time compared to the multiple channel profiles measured bythe scanning device at the same location. As a result, the two sets ofprofiles would not be matching even though they are supposed to.

In order to overcome this problem, some embodiments detect the firstpeak of the channel profiles measured by the mobile device and thenprocess the profiles such that the profiles are shifted in time to makethe first peak appear at time 0. This process is performed on themultipath channel profiles stored in the reference database as well andthereby eliminating the time shift problem caused by the clocks that arenot synchronized. When this process is preformed on the profilesillustrated in FIG. 3, the time T₁ would be shifted to 0, T₂ to (T₂−T₁),and T₃ to (T₃−T₁). Applying the method to the entries in table 605illustrated in FIG. 6, the following time shifts are resulted:T₁₁(x₀,y₀,z₀) to 0, T₁₂(x₀,y₀,z₀) to T₁₂(x₀,y₀,z₀)−T₁₁(x₀,y₀,z₀),T₁₃(x₀,y₀,z₀) to T₁₃(x₀,y₀,z₀)−T₁₁(x₀,y₀,z₀), . . . ,T₆₁(x_(n),y_(n),z_(n)) to 0, T₆₂(x_(n),y_(n),z_(n)) toT₆₂(x_(n),y_(n),z_(n))−T₆₁(x_(n),y_(n),z_(n)), etc. The same process isalso applied to the mobile device's channel profiles before the positionis calculated through matching and interpolation.

As described above by reference to FIG. 3, each peak in a profilecorresponds to a real or virtual transmitter. The above process of timeshifting the position of the first peak eliminates the need for clocksynchronization, but it also eliminates one of the peaks/transmitters.When a position of a mobile device is calculated in two-dimensionalspace, there are two unknown variables for the mobile device's position,for example, (x, y). Since the process described above shift the peaksin the profile to eliminate time synchronization requirements, thereneeds to be a minimum of two peaks after time shifting from all theaccess points in order to find values of the two unknown variables, xand y.

In these two-dimensional cases, three peaks of a profile for a channelbetween a single access point and a mobile device are sufficient to findthe mobile device's location, since after time shifting the profilestill has two peaks corresponding to two virtual transmitters.Alternatively, two profiles each with two peaks are sufficient to findthe mobile device's two-dimensional location because even after timeshifting of the peaks there will be one peak left for each profile,leaving two peaks to figure out the two unknown variables. In thesecases, one peak left for each profile represents one virtual transmitterfor each access point after the time shifting.

Likewise, when calculating a location of a mobile device inthree-dimensional space, there are three unknown variables for themobile device's coordinates, (x,y,z). In these three-dimensional cases,there needs to be a minimum of three peaks after time shifting of allthe profiles for the channels between all the access points and themobile device.

Another factor that can improve the accuracy of the matching andinterpolation used in some embodiments is normalization of the energy ofthe channel's multipath profile. Referring back to table 605, the peaksof the profile of each access point that are extracted are normalized.For instance, an access point with a MAC ID of ID₁, the three peakamplitudes are normalized using an equation,A₁₁(x₀,y₀,z₀)²+A₁₂(x₀,y₀,z₀)²+A₁₃(x₀,y₀,z₀)²=1. Likewise, an equation,A₆₁(x_(n),y_(n),z_(n))²+A₆₁(x_(n),y_(n),z_(n))²=1, can be used tonormalize the peak amplitude values of an access point with MAC ID ofID₆.

In other embodiments, other signal characteristics are used when findinga position of a mobile device by interpolation and matching. Forexample, a scanning device calculates the ratio of the power of thesignals from two access points and records the ratio in the referencedatabase in addition to the other types of characterization data. Amobile device can also calculate the ratio of the power of the twosignals it receives. Likewise, other types of characteristics such asthe time difference of arrival can be calculated and then compared towhat's stored in the reference database. Using the time difference ofarrival of transmission from different access point to find location ofa mobile device would require time synchronization of the access points.Another alternative utilized in some embodiments is to use the signalreceived from one access point and calculate the time difference betweenthe first peak and second peak, first peak and third peak (or secondpeak and third peak), etc. Using the signal from one access pointeliminates the need for time synchronization of the access points ortime synchronization of the mobile with any access point. Accordingly,in these embodiments, the location of the mobile station is determinedwithout synchronizing the access points with each other or with themobile device.

FIG. 10 conceptually illustrates a process 1000 performed by a positionmodule such as position module 805 described above by reference to FIG.8. In some embodiments, the process 1000 is performed in order to findposition of a mobile device. In some embodiments, the position modulecan reside on the mobile device. In other embodiments, the positionmodule is on a network server. In different embodiments, the positionmodule may reside on any other server that the mobile device or thenetwork server can communicate with.

In some embodiments, process 1000 starts when a user of a mobile deviceequipped with a number of different types of radio receivers directs themobile device to find out the position of the user and the mobiledevice. The user can direct the mobile device by entering inputs, suchas such as clicking a mouse button or tapping a touchscreen to select aUI item, and/or pressing keys on a keyboard, etc. In other embodiments,the process 1000 starts when the mobile device initiates the positionfinding automatically without being directed by the user.

Process 1000 initially acquires (at 1005) signals from all radioreceivers of the mobile device. The mobile device in some embodimentsmay be equipped with receivers to receive different types of radiosignals from different sources transmitting the signals in differentstandards. For example, the mobile device may receive signals in GPS,cellular, WLAN 802.11, Bluetooth®, WiMAX, HD Radio™, UWB and 60 MHzstandards. Most radios regularly transmit beacon signals, while othersmay go into a power save mode and require the mobile device to send outa probe request first.

Next, the process extracts (at 1010) characteristics of all the signalsreceived by all of the mobile device's receivers. The processcharacterizes each received signal using one or more signalcharacterization parameters such as channel FFT coefficients, time ofarrival delay using preamble correlation, beacon signal strength, etc.The details of extracting characteristics of signals are described aboveby reference to FIGS. 3-6. Each signal has a unique identifier thatidentifies the source of the signal. Such identifiers in someembodiments include MAC ID for WLAN, cell tower ID for cellular,Satellite ID for GPS, etc. The process then stores the extractedcharacteristics of the received signals and their correspondingidentifications of signal sources in the mobile device's memory. Themobile device's memory may include magnetic disks, optical disks, randomaccess memory, read only memory, flash memory devices, etc.

Process 1000 then determines (at 1015) whether the mobile device or anetwork server is going calculate the position of the mobile device.That is, the process determines whether the position module runs on themobile device or on the network server. The position module is used tocompare the mobile device's channel characteristics with those in thereference database in order to find the mobile device's position.

When the process determines that the position module is not running onthe mobile device (i.e., when the process determines that the mobiledevice does not do the position calculation), the process proceeds to1035 which will be described below. Otherwise, the process determinesthat the position module is running on the mobile device (i.e., theprocess determines that the mobile device does the positioncalculation). Then the process accesses (at 1020) the reference databasewhich stores signal characteristics. In some embodiments, the processdirects a local server of the mobile device to make a wired or wirelessconnection using cellular, public hotspot WLAN, other wireless method toa networked server and after authentication requests and downloads asubset of the data from the tables stored in the centralized referencedatabase. Since the approximate location of the mobile device is knownfrom prior position calculations or from the beacon signals and celltower signals the mobile devices receive, only the signalcharacterization data measured at the coordinates near the approximatelocation of the mobile device are retrieved.

Next, the process calculates (at 1025) the mobile device's positionusing interpolation (or extrapolation) and matching. An exemplaryinterpolation technique that the process uses in some embodiments isdescribed above by reference to FIG. 9. Since there are potentiallyseveral types of radios, and several characterization parameters for anygiven channel, there are different procedures to calculate a position.In some embodiments, the process picks a particular radio (e.g.,802.11), finds the position using each of the available channelcharacteristics of that radio, and then weights these positions tocalculate a final position using that radio alone. The process repeatsthis procedure for other radio types (e.g., GPS, WiMAX, etc). Thecalculated positions based on the data for each of those radio types arethen weighted. The process calculates a position by averaging theweighted positions to come up with a position of the mobile device. Inperforming these position calculation using all radios and all channelcharacteristics, the process in some embodiments may utilize thefollowing pseudo code:

For each “radio type” { For each “channel characteristics parameter” {Calculate position using database interpolation; } Calculate weightedaverage position for all “channel parameters”; } Calculate weightedaverage position for all “radio types”;The procedures represented by this pseudo code are described in detailfurther below by reference to FIG. 11.

In some embodiments, the weights used in averaging calculated positionsof a given radio using different channel characteristics can be based onthe reliability and accuracy of given characteristics. For example, whenthe accuracy of position calculation using channel FFT coefficients arehigher than calculations using other characteristics, the calculatedpositions using channel FFT coefficients are given a higher weights.Calibration experiments can be carried out where the position of themobile device is known and the calculated positions of the mobile device(using different signal characteristics of a given radio type) arecompared to its known position. This can lead to appropriate weights fordifferent channel characteristics.

Likewise, in some embodiments, the weights used in averaging calculatedpositions using different radio types can be based on the reliabilityand accuracy of each radio. For example, if the WLAN radios providebetter positioning accuracies than GPS radios do, the positioncalculated based on characteristics of WLAN radio signals are given ahigher weight compared to the position calculated based oncharacteristics of GPS radio signals. Again, calibration experimentswith known locations of the mobile device can be carried out to find theappropriate weights for each radio type.

In some embodiments, calculated positions for each radio type arenormalized based on the sensitivity levels to improve accuracy ofposition calculation. A sensitivity level is the minimum received signalpower required to find channel characteristics and/or to decode signals.

Next, the process filters (at 1030) the calculated location of themobile device. This operation 1030 is optional. The process filters andsmoothes the calculated location of the mobile device based on previouspositions of the mobile device. The mobile device can calculate themobile device's position at various time instances. The process cancalculate the velocity of the mobile devices using two or more positionsat which position calculations were made and the time the mobile devicetook to get to one position from another. Velocity information may alsobe computed from signal amplitude information and Doppler effects.Previous calculated positions and velocity information can be used tofilter and smooth the position calculated using interpolation. Thissmoothing mechanism can further reduce positioning errors. The filteringalgorithms may be simple algorithms like linear 1-tap filters in someembodiments. In other embodiments, the more complex algorithms such asKalman filtering are used. The process then proceeds to 1055 and 1060which are described below.

When the process determines that the position module is not running onthe mobile device (i.e., when the process determines that the mobiledevice does not do the position calculation), the process transfers (at1035) the extracted channel characteristics data from the mobile deviceto a network server. Then the process accesses (at 1040) the referencedatabase by the network server to obtain signal characteristics storedin the database as described above for the operation 1020. The processdownloads a subset of the data from the centralized reference database.The process calculates (at 1045) the mobile device's position by thenetworked server using interpolation and matching techniques asdescribed above for the operation 1025. The process then optionallyfilters (at 1050) the calculated position or location of the mobiledevice by the network server's position module and sends the filteredposition back to the mobile device. In some embodiments, the process maysend the position information to the mobile device without filtering theposition information.

The process then optionally reports (at 1055) database inconsistenciesto the network server so that the server corrects or initiatescorrection of data stored in the reference database. In someembodiments, the process flags the inconsistencies to the networkedserver. After the process calculates the location of the mobile device,the process compares the measured channel characteristics data withthose stored in the reference database and notify the networked serverif there are inconsistencies found. The inconsistencies between themeasured data and stored data can be caused when some previouslyexisting transmitters that were transmitting radio signals to thelocation of the mobile device are removed, new transmitters are addedand transmit additional signals to the location, new buildings are builtand obstruct signal paths, previous existing buildings are demolishedand no longer block or reflect signals. Then the networked server canmodify the centralized database entries or send a service request to themoving scanner to scan that area again in order to update the database.

The process may also optionally use (at 1060) the calculated positionfor navigation. The process may pass the calculated position informationto a navigation module that uses the calculated position for navigationpurposes. This module superimposes the calculated position onto a mapand provides navigational features such as giving directions anddisplaying points of interest. The process then ends. It should beappreciated that process 1000 does not use triangulation whendetermining the location of the mobile device.

One of ordinary skill in the art will recognize that the specificoperations of process 1000 may not be performed in the exact order shownand described. The specific operations may not be performed in onecontinuous series of operations, and different specific operations maybe performed in different embodiments. Furthermore, the process could beimplemented using several sub-processes, or as part of a larger macroprocess. In addition, some operations can be done by a network servewhile other operations can be done by a mobile device. For instance, insome embodiments, operations 1005-1030 and 1055-1060 are performed bythe mobile device while other operations are performed by another device(such as a server). One of ordinary skill in the art will recognize thatother combinations for perform operations of process 1000 by the mobiledevice and a remote device are also possible.

FIG. 11 conceptually illustrates a process 1100 performed by a positionmodule such as position module 805 described above by reference to FIG.8. Specifically, FIG. 11 illustrates in detail position calculationsperformed by a position module in some embodiments using matching andinterpolation. In some embodiments, the position module can reside on amobile device of which the position is to be determined. In otherembodiments, the position module is on a network server. In differentembodiments, the position module may reside on any other server that themobile device or the network server can communicate with.

In some embodiments, process 1100 starts when the position module hasextracted characteristics of all the signals received by all of themobile device's radio receivers. The position module has characterizedeach received signal using one or more signal characterizationparameters such as channel FFT coefficients, time of arrival delay usingpreamble correlation, beacon signal strength, etc. The position modulehas performed extraction of characteristics for all radio types whichinclude GPS, cellular, WLAN 802.11, Bluetooth®, WiMAX, HD Radio™, UWBand 60 MHz, etc. In addition, the position module has accessed areference database and retrieved characterization data stored in thedatabase.

As shown, process 1100 initially picks (at 1105) a radio type (e.g.,802.11) to calculate position of the mobile device using the extracteddata and the retrieved data of signals of the type. Then the processpicks (at 1110) a characterization parameter (e.g., channel FFTcoefficients) to calculate position of the mobile device. In someembodiments, the mobile device does not include a cellular radio (e.g.,a PCS or media player) but the mobile device is capable of communicatingin short range by using a short range radio such as WLAN, Bluetooth,UWB, etc. In some embodiments, the mobile station is able to communicatevia cellular and a short-range radio. Some embodiment utilize process1100 and other techniques disclosed in this specification to locate amobile device when the mobile device is in an indoor place wherecellular connectivity is weak or is not available (such as a building ora mall) or when the mobile device is in an outdoor location wherecellular connectivity is weak or is not available (e.g., inside a tunnelor urban canyons). In addition, short-range access points (where therange of the access point is less than 1000 feet) are low-cost, wideavailable, and closely placed. In contrast, cellular towers are aimed atmedium and long-range communication and are typically several miles awayfrom each other.

Next, the process compares (at 1115) the characterization data extractedat the mobile device's position with the characterization data of thepositions retrieved from the database to find a match among theretrieved data. In some embodiments, the characterized data stored inthe database include a timestamp. For instance, when the characterizeddata were collected from a time varying source (such as an orbitingsatellite), the signals are collected from the time varying source andthe extracted parameters are stored in the database with a timestamp(e.g., time of day). In some embodiments, process 400, compares thecharacterization data extracted from the mobile device's position withcharacterization data retrieved from the database that has a timestampclose to the time that the mobile device's characterization data iscollected (e.g., a close time of day timestamp). In other embodiments,several values from the database are weighted based on proximity oftheir timestamps to the time that the mobile device's characterizationdata is collected in order to provide a value to compare with the Mobiledevice's characterization data.

Next, the process determines (at 1120) whether there is an exact match.When the process determines that there is an exact match, the processdetermines (at 422) the calculated location of the mobile device (forthe picked parameter and radio type) as the location information orposition associated with the matched data. The process then proceeds to430 which is described below. Otherwise, the process calculates (at1125) the position of the mobile device using interpolation orextrapolation. An exemplary interpolation technique is described aboveby reference to FIG. 9.

Next, the process determines (at 1130) whether there is any otherparameter to use in calculating the position of the mobile device. Theprocess goes through the parameters used to extract characteristics ofthe received signals as well as the parameters used in thecharacterization data retrieved from the reference database. To use aparameter, it must exist in both the extracted data and retrieved data.

When the process determines that there still is a characterizationparameter available to use, the process returns to 1110 to pick thatparameter and uses it to calculate the position of the mobile device.Otherwise, the process calculates (at 1135) the weighted averageposition for all channel characterization parameters. As described aboveby reference to FIG. 10, the weights used in some embodiments inaveraging the calculated positions are based on the reliability andaccuracy of given characteristics. This average position is the positioncalculated for the picked radio type.

The process then determines (at 1140) whether there are signalcharacteristics of other radio types. The process goes through both theextracted and retrieved data to see if a radio type existing in bothdata has not been used yet to calculate the position of the mobiledevice. To use the signal characteristics of a radio type, the radiotype must exist in the extracted data and retrieved data.

When the process determines that there still is a radio type of whichthe signal characteristics are yet to be used in position calculation,the process returns to 1105 to pick that radio type to performcalculation. Otherwise, the process calculates (at 1145) the weightedaverage position for all radio types that the process has used tocalculate the position. This average position is the position calculatedusing all available characterization parameters and radio types. Theprocess then ends.

The specific operations of process 1100 may not be performed in theexact order shown and described. The specific operations may not beperformed in one continuous series of operations, and different specificoperations may be performed in different embodiments. Furthermore, theprocess could be implemented using several sub-processes, or as part ofa larger macro process, for example, process 1000 described above byreference to FIG. 10.

IV. Alternative Embodiments to Populate Channel Characteristics Database

As described above, a moving scanner equipped with radio receivers goesaround different locations in an area of interest and characterizessignals that it receives to populate a centralized reference database insome embodiments. In other embodiments, instead of having a movingscanner perform signal characterization at different locations, fixedradios that are installed at known coordinates in the areas of interest(e.g. GPS for outdoor urban or downtown areas, WLAN for indoor locationsand tunnels, etc.) characterize signals that they receive and populatethe reference database. In these embodiments, the need for a movingscanner is eliminated.

FIG. 12 conceptually illustrates the use of fixed radios instead ofmoving scanners in some embodiments. As shown, public hotspot accesspoints 1200 with both GPS and WLAN transceiver radios are installed atknown coordinates on posts and buildings and connected to a networkbackbone 1205 either wirelessly or through wires. FIG. 12 alsoillustrates private access points 1210 that send out beacon signals andwhose channels can be characterized. Unlike the public access points1200, these private access points 1210 in some embodiments are not usedfor transmitting those data to populate a reference database. In otherembodiments, private access points can be used for signalcharacterization and data transmission, with permission or agreement bythe provider or owner of the access points.

To implement those embodiments where public hotspot access pointsinstalled at known coordinates collect signal characterization data andpopulate a reference database, the service providers, such as AT&T™,which already provide WLAN access points for hotspot areas, would needto add GPS capability to the access points that they operate. Whilehotspot access points 1200 illustrated in FIG. 12 have only GPS and WLANradios, one of ordinary skill in the art would realize that the accesspoints can have other types of radios such as Bluetooth®, WiMAX, HDRadio™, UWB, 60 GHz and cellular to receive radio signals of thosetypes. Also, these radios are added to existing access points to enablethe access points to characterize signals and populate the referencedatabase. However, these radios can be installed at new locations wherethere are no access points installed previously so that there are moremeasurements points in the area of interest in addition to the accesspoints locations. These radios can also be installed at cell towers sothat the cell towers can characterize different types of radio signalsand populate the reference database.

The GPS-equipped access points in some embodiments receive GPS signalsfrom satellites 1215 and characterize the GPS channels at theirlocations. Thereby the access points in these embodiments effectivelyperform the same function as a moving scanner which characterizes GPS orother radio signals it receives at grid sampling points within an areaof interest as described by reference to FIG. 5 above. The locations ofthese access points 1200 are effectively the grid sampling pointsillustrated as locations marked with X's in FIG. 5.

In some embodiments, the GPS transceivers of the public access points1200 illustrated in FIG. 12 communicate with the satellites 1215 thatthey see, characterize their channel characteristics (e.g., delay,phase, range to satellite, their IDs, etc.) at different time instances,and transmit the characteristics to a network server 1220. In theseembodiments, the network server 1220 processes the channelcharacterization data it received from the access points 1200 furtherbefore storing the time, channel characteristics and positioncoordinates in a centralized reference database 1225.

In some embodiments, the access points equipped with GPS perform thesignal characterization on a continuous basis with the access point'sGPS always running. In other embodiments, the signal characterization isperformed when a mobile device such as mobile device A 1230 is nearbythe access points and requires positioning information from the accesspoints. In these embodiments, the access points' WLAN radios send outbeacon signals that mobile device A's WLAN receiver can detect.Likewise, mobile device A's WLAN radio can send beacon signals that theaccess points' WLAN receiver can detect. Thus, mobile device A 1230 andthe access points 1200 know about each other's presence.

In some embodiments, the public GPS-equipped access points 1200 alsoprovide the mobile device 1230 with assistance data such as satelliteID, almanac, clock correction, ephemeris, approximate time, andapproximate location. Passing these assistance data from the accesspoints 1200 to the mobile device A 1230 ensures that the access points1200 and the mobile device 1230 see the same satellites and experiencesimilar multipath and channel characteristics. For example, when publicaccess point 1 1210 detects mobile device A 1230 by receiving wirelessbeacon signals from mobile device A 1230, the access point tells themobile device which satellite to look at.

In some cases, some of the access points 1200 illustrated in FIG. 12 mayalso need assistance data if they are stationed indoors or if theyreceive weak satellite signals. In such cases, the network server 1220or one of the nearby access points that receive signals from satellites1215 can provide those access points with assistance data such assatellite ID, almanac, clock correction, ephemeris, approximate time,and approximate location.

FIG. 12 also illustrates other embodiments of the invention where,instead of having a moving scanner perform signal characterization atdifferent locations as described above by reference to FIGS. 4 and 5,signal characterization is performed by another kind of a movingscanner, a position-enabled mobile devices with communication radios(e.g., mobile or hand-held devices moved around by humans on sidewalksor inside malls). In these embodiments, a multi-radio mobile orhand-held device uses one radio (e.g., a GPS) to compute its positionand uses another radio (e.g., WLAN) to characterize and populate thereference database for that radio at that position.

In these embodiments, mobile device A 1230 illustrated in FIG. 12 firstuses its GPS radio to find its position, and then uses its WLAN radio tocharacterize the WLAN radio channels it sees in order to populate thereference database. Using its GPS radio to find its position first isdifferent from some embodiments described above by reference to FIG. 10where all of the different radio types of a mobile device are used tocharacterize signals in order to calculate the position of the mobiledevice by comparing the characterization data with the data stored in areference database. While it is certainly possible that mobile device Ause both its GPS and WLAN radios to characterize signals and locatemobile device A by comparing characterization data, in these embodimentsmobile device A's GPS radio is used to compute mobile device A'sposition on its own and mobile device A's WLAN radio is used tocharacterize the WLAN signals and populate a reference database with thecharacterization data. The signal characterization data stored in thereference data are then used by mobile devices such as mobile device B1235 that does not have a GPS to find its location on its own. Mobiledevice B will characterize the signals it received via its WLAN radioand characterize the signals first. Mobile device B will then comparethe data with mobile device A's signal characterization data stored inthe database in order to obtain its location information.

Mobile device A 1230 can employ several different methods to calculateits position. In some embodiments, mobile device A's GPS communicateswith the satellites 1215 to compute its position. However, if mobiledevice A experiences significant multipath and fading in receivingsignals from the satellites, the calculated position can be inaccurate.

In other embodiments, mobile device A characterizes its GPS channelswith each satellite it sees. Interpolation and matching of mobile deviceA's GPS channel characteristics with the reference GPS channelcharacteristics is used to locate mobile device A. In these embodiments,either the mobile device A's position module accesses the referencedatabase and does the calculations or the mobile device transfers itschannel characteristics to a network server using hotspot WLAN, cellularor other connectivity mechanisms and the server's position modulecalculates the mobile device's position. This position calculation inthese embodiments is more accurate than the position calculation done bymobile device A's GPS alone because the calculation is done by comparingits signal characterization data with the data measured by nearbyGPS-equipped access points of which the accurate positions are known andmobile device A experiences similar multipath and fading that thesenearby GPS transceivers experience.

In different embodiments, the fixed location GPS transceivers of theaccess points illustrated in FIG. 12 act as differential correctionreferences. These transceivers communicate with the satellites, computetheir coordinates, and then compare their GPS computed coordinates withtheir known fixed coordinates to find errors for each satellite. Theythen provide differential correction data to other GPS transceivers suchas mobile device A 1230. In some embodiments, mobile device A 1230 canuse the differential correction data from the nearest access point. Inthese embodiments, mobile device A finds the nearest access point basedon the signal power, i.e., the access point showing most signal power tomobile device A is considered nearest to mobile device A. In otherembodiments, mobile device A takes differential correction data from anumber of nearby access points and uses them after weighting the databased on signal power that mobile device A receives from each the accesspoint.

After mobile device A 1230 calculates its position with GPS, it can useits WLAN radio to characterize other WLAN radio communication channelsthat are present at that position such as the channels of the accesspoints. Mobile device A 1230 can transmit to the access points or theaccess points can transmit to mobile device A. It should be noted thatboth downlink and uplink channel characterizations are possible. Whenmobile device A and an access point use the same frequency band and eachuse a single antenna, characterization with access point transmittingand mobile device A receiving should give the same results as mobiledevice A transmitting and the access point receiving. The processing ofthe channel characteristics can be performed by the mobile device, theaccess point, or a network server. The processed characterization dataare then uploaded and stored in the reference database 1225. Mobiledevice A can repeat this channel characterization process at differentlocations as it moves around the different positions. In someembodiments, other mobile devices equipped with GPS and WLAN radiosperform these signal characterization process at different locations(i.e., more than one mobile device can perform characterizationprocess), thereby populating the reference database for the coveragearea of the access points and eliminating the need for a moving scannerdescribed above by reference to FIGS. 4 and 5. Implementation of theseembodiments where the mobile devices characterizes signals and populatethe reference database depends on contribution by the users who usesthese mobile devices. That is, the users must direct their devices tocharacterize the signals or the users authorize the service providers toutilize their devices to characterize the signals at differentlocations.

In some embodiments, the public hotspot access points 1200 characterizeall the WLAN communication channels at their respective locations. Forexample, public hotspot access point 1 1200 can measure characteristicsof its WLAN communication channels between it and all the other publicWLAN access points, as well as the channels between it and private WLANaccess points 1210. Such channel measurement by the public hotspotaccess points 1200 can be repeated frequently and the correspondingentries in the reference database are updated accordingly.

Mobile device B 1235 that does not have a GPS but does have a WLAN radiouses its WLAN radio to locate its position in the hotspot area bycharacterizing the WLAN signals it receives and comparing thecharacterization data with those stored in a reference database. Forinstance, as mobile device B 1235 enters a WLAN hotspot coverage area,it characterizes its WLAN communication channels with the access points.A position module matches the data with channel characteristics storedin the reference database to find mobile device B's position. In someembodiments, the position module runs on mobile device B 1235. In otherembodiments, the position module runs on the network server 1220. In acommercial setting, a service provider that is providing the wirelesshotspots may provide the network server, the reference database, and thepositioning module as a service.

V. Multiple Antenna Systems

As described above, some embodiments of this invention rely oncharacterization data of signals that propagate in multipath to achievepositioning accuracy. In some cases, however, signals do not propagatein multipath (e.g., an environment where there are not enough obstaclesand reflectors to cause the signals to reach the receivers inmultipath). In other cases, the channel profile of an access point at acertain location only has a direct line of sight propagation with onepeak or one multipath peak. In these cases the profile cannot contributeto the proposed methods of position calculation of the invention. Insuch cases, some embodiments of the invention use Multiple InputMultiple Output (MIMO) systems that have multiple antennas to generate amultipath profile signature. FIG. 13 conceptually illustrates an exampleof a system that uses multiple antennas to generate multipath in someembodiments. Specifically, FIG. 13 shows an access point 1300 and tworeceivers (e.g., mobile devices) 1310 and 1315. The access point in thisexample has four antennas 1305. In some embodiments, antennas 1305transmit signals at the same time. In other embodiments, antennas 1305transmit signals close together in time with a known time separationpattern. In some embodiments, MIMO systems add cyclic delays even forthe transmitters that are meant to transmit at the same time.

Each mobile receiver receives the superposition of the four wavesreceived from each access point antenna. However, since the antennas1305 are separated in space, the waves travel different distances toreach the mobile receiver. Thus, the waves arrive at different timespartially as a result of traveling different distances and partially asa result of any transmit time differences between the antennas 1305,thereby creating a profile that resembles a multipath profile. Eachantenna transmitting separately represents a separate channel. Thus thewaveform received at the receiver can be viewed as a multi-channelprofile.

FIG. 14 conceptually illustrates two graphs 1410 and 1415 thatillustrate the multichannel profiles created as a result of theseparation of the antennas at positions (x₁,y₀,z₀) and (x₂,y₀,z₀)respectively. If the transmit time separation between the antennas 1305of the access point 1300 is fixed for different positions of mobiledevices (e.g., at mobile position (x₁,y₀,z₀), antenna 2 transmits 100 msafter antenna 1, antenna 3 transmits 100 ms after antenna 2, etc, and atmobile position (x₂,y₀,z₀) antenna 2 transmits 100 ms after antenna 1,antenna 3 transmits 100 ms after antenna 2, etc.), the shape of themultichannel profile and the position of the peaks in the multichannelprofile of the waves that the mobile receivers 1310 and 1315 receive aredependent on the position of the mobile device, as illustrated in graphs1410 and 1415 of FIG. 14. For example, the waves from the antennas 1305arrive at the receiver 1310 at different times because the waves traveldifferent distances to reach the receiver 1310. Transmissions fromantennas that are further away from the receiver travel greaterdistances and as a result arrive at later times with smaller amplitudes.In free space, signals experience free space loss, where the receivedpower is proportional to 1/d² where d is the distance between thetransmitter and the receiver. Likewise, the multichannel profile 1415 atposition (x₂,y₀,z₀) that is further away from the antennas 1305 than(x₁,y₀,z₀) shows four peaks, but the peaks arrive with smalleramplitudes and at later times than the peaks of the multichannel profileat position (x₁,y₀,z₀) as illustrated in graphs 1410 and 1415. Alsosince the mobile is at a more oblique angle at 1315 than at 1310 andhence waves from more distant antennas have further to travel, the fourpeaks in graph 1320 are more separated in time compared to the peaks ingraph 1315 for the multichannel profile at position (x₁,y₀,z₀).

While FIG. 13 illustrates an embodiment that uses a MIMO access point,other embodiments have a single-antenna access point and MIMO receivers.Yet other embodiments have MIMO access points and MIMO receivers. Insome embodiments, when the mobile device has multiple receiver antennas,the location of each mobile device antennas is separately determinedusing any of different disclosed methods. Thus, each mobile antenna istreated as an independent channel and there are multiple peaks as aresult of obstacles and/or as a result of the access point havingmultiple antennas. The location of the mobile device is then determinedbased on the location of the mobile device antennas (e.g., as a weightedaverage of the location of each mobile device antenna). If the accesspoint has only one antenna transmitting to a mobile that has multipleantennas, and there are no obstacles to generate multiple peaks then thescanner's radio that populates the database must have the same antennaconfiguration as the mobile.

As describe above, the access point antennas can transmit at presetdifferent times which manifest themselves at the receiver as fixedadditional delays for each antenna path. Then, in these embodimentswhere there are MIMO transmitters and/or MIMO receivers, the environmentis scanned and the characteristics of the communication channels arestored in a reference database. The only difference between theseembodiments and the embodiments where the multipath profiles areexperienced because of the obstacles and reflectors is that thecommunication channels are MIMO. As described above by reference to FIG.2, reflections from obstacles created multipath waves that could beviewed as originating from virtual transmitters. In the exampleillustrated in FIG. 13, there are no obstacles to create multipaths.Instead, spatially separated antenna transmitters 1305 create themultichannel profiles that can be viewed as multipath profiles.

In some embodiments, MIMO systems are used in environments where thereare obstacles that cause reflections and multipaths. In someembodiments, each antenna transmits separately and the receiver receiveseach antenna transmission separately. Then in these embodiments eachantenna of a MIMO transmitter can be treated as a separate transmitterwith a separate location like the transmitters described above byreference to FIG. 2, and the antennas effectively improve the accuracyby providing more channel characteristics data such as those illustratedin FIG. 3. In other embodiments, the antennas of a MIMO system transmittogether and there are also reflectors causing multipaths. When theantennas transmit together, some embodiments characterize the signalsfrom all transmitters of a MIMO transmitter as a single transmissionchannel. In these embodiments, the channel profile of the MIMOtransmitter provides even more peaks (some due to the multiple antennasand some due to reflections), which increases the accuracy of someembodiments of this invention. These embodiments are in effect acombination of the embodiments illustrated in FIG. 2 and the embodimentsillustrated in FIG. 13.

VI. Extracting Channel Characteristics for WLAN 802.11 Signals

This section uses the wireless LAN IEEE 802.11a standard to demonstratesome methods of how to extract some of the channel's characteristicssuch as the multipath profile, channel FFT coefficients, time of arrivaldelay using preamble correlation, and time of arrival delay using RSSItransition in some embodiments. However, one of ordinary skill in theart would realize that the disclosed methods can be applied to anysystem that has a preamble structure such as Bluetooth®, WLAN, cellular(2G, 3G, 4G), WiMAX, HD Radio™, UWB, and 60 GHz standards. The proposedmethodology works as long as both the transmitter and receiver use thesame standard.

FIG. 15 illustrates the frame format in the 802.11a standard. As shown,the frame includes a Physical Layer Convergence Protocol (PLCP) Preamblefield 1505 (which includes 12 symbols), a SIGNAL field 1510 (whichincludes one OFDM symbol) and a Data field 1515 (which includes avariable number of OFDM symbols). The LENGTH and RATE of the Datasymbols are included in the SIGNAL field and are required to decode theDATA symbols. The standards document describes these fields in detail.

FIG. 16 illustrates the OFDM training structure in more detail. Asshown, the PLCP preamble field 1505 includes 10 short training sequencesfollowed by a guard interval and two long training sequence. As shown,t₁ to t₁₀ denote short training symbols, GI2 denotes the guard interval,and T₁ and T₂ denote long training symbols. The PLCP preamble 1505 isfollowed by the SIGNAL field 1510 and DATA 1515. The total traininglength is 16 μs.

The short training sequence can be used for Automatic Gain Control(AGC), diversity selection, timing acquisition, and coarse frequencyacquisition. The long training sequence can be used for channelestimation and fine frequency acquisition. In some embodiments, channelestimation is used to calculate the time elapsed from the frame boundarycorresponding to the earliest path from a transmitter to a receiver.Initially, some of the 10 short preamble sequences are used for AGC,frequency and time estimation and the remaining are used for timecorrelation. These remaining short preambles give coarse timingsynchronization and narrow the range of correlation. The phase of thecorrelation evaluated at the peak also gives the coarse frequencyestimate. Then the correlation of the long sequence gives a multipathprofile such as the one illustrated in FIG. 3. The multipath profilerepresents the channel characteristics and can be stored in a databasetable. The earliest delay peak determined through this correlationcorresponds to the direct line of sight distance between the transmitterand receiver. When there is no line of sight, the correlation peakcorresponds to the shortest multipath. All the peaks' amplitudes, phasesand time of arrivals of the multipath profile can also be extracted asillustrated in FIG. 3 and stored in a database table such as table 605illustrated in FIG. 6.

FIG. 17 conceptually illustrates a system 1700 of some embodiments thatuses the known transmission pattern of the training structure todetermine the time delay between a transmitter base station and a mobilereceiver. As shown, system 1700 includes a transmitter 1705 and a mobiledevice's receiver 1715. An example of mobile device's receiver is an802.11a receiver. One of ordinary skill in the art would realize thatspecific details of FIG. 17 are given as an example and it is possibleto use different circuit implementations to achieve the same resultswithout deviating from the teachings of the invention. Furthermore, theexample of FIG. 17 uses the wireless LAN IEEE 802.11a standard todemonstrate methods of how to extract some of the channel'scharacteristics such as the multipath profile, channel FFT coefficients,time of arrival delay using preamble correlation. However, one ofordinary skill in the art would realize that the disclosed methods canbe applied to any system that has a pre-amble structure such asBluetooth®, GPS, WLAN, cellular (2G, 3G, 4G), WiMAX, HD Radio™, UWB, and60 GHz standards. The proposed methodology works as long as both thetransmitter and receiver use the same standard.

As shown, the receiver's baseband calculates a channel's multipathprofile (see Marker B in FIG. 17), channel's FFT coefficients (seeMarker A in FIG. 17), and time of arrival delay using preamblecorrelation (see Marker D in FIG. 17). The receiver's baseband usescorrelation with the preamble training structure to calculate thechannel's multipath profile and uses FFT to transform it into thechannel's FFT coefficients. In this example the receiver employs directconversion where the carrier frequency is directly converted to twobaseband signals; in-phase I and quadrature Q, which are then sampled byAnalog-to-Digital-Converters (ADC) (see Markers I and Q in FIG. 17). Themethod, however, applies to other receiver architectures too, such asheterodyne or super heterodyne, where an ADC is used to sample the fullsignal bandwidth of interest.

The antenna 1705 of the access point 1710 transmits an RF waveform. Theantenna 1715 of the receiver j receives the RF signal after a delaycorresponding to the distance between the two. The Low Noise Amplifier(LNA) 1720 amplifies the received RF signal without increasing the noiselevel. A Phase Lock Loop (PLL) 1725 generates a clock for downconversion as well as the clock (f_(s)) to sample the analog signal atthe Analog to Digital Converter (ADC) 1730 input. The PLL 1725 clock isalso used for an N bit counter register 1735 that stores time delays.This register counts from 0 to (2N−1) and then wraps around back tozero. Therefore, the size of this register, N, should be large enough torepresent the time delays that are expected so that time wrap around canbe avoided. It should be noted that once the counter wraps around tozero, that portion of the time delay is lost. For instance, when thecounter only goes up to 100 ms, a time delay of 115 ms will show up as15 ms in the counter. When T is the largest time delay that is expected,the counter should be designed to be larger than T plus a delta, wheredelta is the maximum expected time offset error. The transmitter alsohas a counter that is synchronized with the counter of the receiverthrough calibration transmissions. When the non-synchronous approach ofshifting the channel multipath profiles to the position of the firstpeak as described above is used, there is no need for the counters orfor their synchronization.

The mixer 1740 uses the RF output of the LNA 1720 and the PLL 1725signal to down convert the RF signal for baseband. The down-convertedsignal is a complex signal with in-phase (I) and quadrature (Q)components (see Markers I and Q in FIG. 17). The filter 1745 rejectsunwanted signals. The ADC 1730 digitizes the signal. A time trackingloop is run so that the ADC sampling points are synchronized with thetransmitted waveform. This means that the sampling points are identicaland hence avoids interference between symbols (Inter-Symbol Interferenceor ISI). The amplitude of the ADC 1730 output may be used for AutomaticGain Control (AGC) 1750 so that front-end RF block settings are adjustedand the ADC output captures the main signal. The short preamblecorrelator 1755 correlates its input signal with the signature of theshort preamble received from known short preamble structure source 1760and its output adjusts the gain control settings. After the shortpreamble correlation is complete then the correlation with the longpreamble structure is carried out.

Referring back to FIG. 16, the long preamble consists of two preamblesymbols, T₁ and T₂. The short pre-amble correlator 1755 also provides acoarse time estimate for the sliding window of the long preamblecorrelator 1785, so that the correlation window starts just before T₁.The long preamble correlator 1785 can correlate the I and Q receivedsignal components against both T₁ and T₂ together (by using thesignature of the long preamble received from known long preamblestructure source 1798). This is a complex correlation and the output ofthe long preamble correlator 1785 therefore is a complex signal withreal and imaginary components. The complex output of this correlator canin some embodiments be stored in memory to represent channel FFTcoefficients, or be used together with extracted pilots to calculatemore accurate channel FFT coefficients. A modulus operation is thencarried out (by the modulus operator 1768) on the output of the longpreamble correlator where the absolute value magnitude of the complexsignal is calculated. The magnitude of the output of the long preamblecorrelator 1785 is marked B in FIG. 17.

FIG. 18 conceptually illustrates (similar to FIG. 3) magnitude output ofa long preamble correlator of some embodiments. Tij denotes the time forthe RF wave to travel from the transmitter's RF antenna i 1705 to thereceiver's RF antenna j 1715. The transmitter's and receiver's front-endRF components (antenna, LNA, filter, ADC) introduce an additional delay,t_(FEdelay), before the signal reaches the long-preamble correlator1785. This delay has a mean value with a small deviation for a givenhardware. Therefore, the delay is treated as a constant in someembodiments. Also, the long preamble signal introduces an additionalfixed delay of 16 μs since it has to wait for the arrival of both T₁ andT₂ before performing the correlation (8 μs for the first 10 shortpreambles, 1.6 μs for the guard interval, 3.2 μs for T₁ and 3.2 μs forT₂. It should be appreciated that when the correlation is done onlyagainst T₁, there is no need to wait for T₂. However, using both T₁ andT₂ increases the signal to noise ratio and improves the accuracy of thepeak detection). Thus the location of the correlator's first peakcorresponds to (T_(ij)+16 μs+t_(FEdelay)), which is travel time delayT_(ij) subject to a fixed offset. The amplitude of the peaks and thevalue of the counter register 1735 corresponding to the location of thepeak is stored in memory 1770.

After a time delay that is a system parameter, the system performs FFTon the OFDM signal symbol. The MAC address (i.e., MAC ID) of thereceived RF signal, which is in the SIGNAL symbol, is computed with anOFDM FFT 1758 and a MAC and transmission time decoder (Mac & Tx timedecoder) 1750. The OFDM FFT 1758 uses the output of the long preamblecorrelator 1785 in order to determine when to start the FFT. The OFDMFFT also discards the cyclic prefix. The MAC address is then saved inmemory 1770 so that stored time delays correspond to a particular accesspoint's MAC address. The transmission time, represented by thetransmitter's counter value at transmission, can be stored in the SIGNALsymbol. Mac & Tx Time decoder 1750 also retrieves the transmission timewith an FFT and stores it in memory 1770. The difference between thetransmitter's counter value at transmission and the receiver's peakdetection counter value represents time of arrival plus system delaysand synchronization offsets (which may be calibrated and compensatedfor). The data is also decoded with an equalizer (equalizer details arenot shown in FIG. 17) which uses the channel coefficient estimates to dodivision and multiplication in the FFT domain to decode the data. Insome embodiments, the correlators do not have to run all the time andmay go to sleep once the peak detector finds the first peak.

In 802.11 WLAN OFDM implementation, four of the subcarriers arededicated to pilot signals. A total of 52 subcarriers are used where thepilots are inserted at positions −21, −7, 7 and 21. The remaining 48positions are used for data and contain no channel information. Havingthese pilot signals inserted at those positions makes coherent detectionrobust against frequency offset and phase noise. The pilot signals aremodulated by a pseudo binary sequence to prevent the generation ofspectral lines. If packet sizes are long then the channel can change andone can use these pilots to update channel and time delay estimates. Thepilots go through the channel and provide one measurement of the channelat their positions. The long preamble also goes through the channel andis then correlated with the original preamble structure. The output ofthe long preamble correlator 1785, therefore, provides a secondmeasurement of the channel at the pilot positions.

Referring back to FIG. 17, some embodiments optionally (shown with thedashed arrows) extract the pilots by an FFT and use them to improve thechannel estimate and update the peak detector's output. To do this theextracted pilots are fused (by the fuse component 1790) with the FFT(calculated by FFT module 1792) of the previously stored long preamblecorrelator 1785 output. This fusion step involves using weights tochange the values of the FFT of the long preamble correlator 1785 outputat the pilot locations. The result then goes through an inverse FFT(IFFT) 1794 step and the improved estimates are then fed back to modulusoperator 1768 and peak detector 1765. Channel FFT coefficients arecalculated by FFT module 1775 and stored in memory 1770.

A. Preamble Correlation and Pilot Channel Estimate

The following description uses an example where OFDM uses 52 frequencysubcarriers with 4 pilot subcarriers (−21, −7, 7 and 21) and uses a 64point FFT/IFFT, where the received signal is in the time domain and thereceiver performs FFT to take the signal back into the frequency domain.Typically coefficients 1-26 are mapped to the same FFT/IFFT inputs,coefficients −26 to −1 are mapped to 38-63 FFT/IFFT inputs, and theremaining inputs (27 to 37 and 0) are set to 0. The received signal of achannel is convolution of the input signal with the impulse response ofthe channel. In the frequency domain this means that the FFT of thereceived signal is multiplication of the FFT of the input signal withthe FFT of the impulse response of the channel. Therefore, the FFT ofthe received long preamble signal is multiplication of the FFT of theinput signal with the FFT of the channel and is expressed by thefollowing equation:

FFT of the received long preamble=LmCm

where Lm is the FFT of the transmitted long preamble signal; Cm is thechannel FFT; and m is the subcarrier frequency index.

The long preamble correlator convolves the received signal with theconjugate of the long preamble (since the signal is a complex number).The result (shown as Marker V in FIG. 17) is expressed in the followingequation:

V _(m) =|L _(m)|² C _(m)

where mε[−32, . . . , 31].

The above Equation for V_(m) gives one estimate of the channel. Supposethat P_(m) are the transmitted pilots and P′_(m) are the extractedpilots in FIG. 17. These extracted pilots also provide channelmeasurements at the pilot locations (indexes m equal to −21, −7, 7 and21) as expressed in the following equation:

P′ _(m) =ρ·C _(m) P _(m)

where mε[−21, −7, 7, 21] and 6 is a complex number that includesamplitude and phase offsets due to the effects of the channel andtransmitter/receiver front-ends. Non-pilot index values of m containdata.

Some embodiments minimize the difference between these two channelmeasurements in order to calculate the offset, ρ, and use that toimprove the channel estimate and update the peak detector's output. Anexample is expressed in the following equation:

${\min_{\sigma}{\sum\limits_{m}\frac{P_{m}^{\prime}}{\sigma \; P_{m}}}} - \frac{V_{m}}{{L_{m}}^{2}}$

where mε[−21, −7, 7, 21].

This minimization results in a value for the offset ρ, which can then beused to modify V_(m) at the 4 pilot locations. A weight, τ, can then beused to fuse the original measurement Vm with the modified version atthe pilot locations as expressed in the following equation:

$w = {{\tau \; V_{m}} + \lbrack \frac{( {1 - \tau} ){L_{m}}^{2}P_{m}^{\prime}}{\sigma \; P_{m}} \rbrack}$

where mε[−21, −7, 7, 21]. At non-pilot positions the weight, τ, is 1 sothat the original V_(m) values are retained. The weighted fused value(shown as Marker W in FIG. 17) then goes through an IFFT step 1794 andfed back to the peak detector 1765 through the modulus operator 1786.FIG. 17 shows the channels FFT coefficients are also stored in memory.This can be done by performing an FFT on the improved pilot channelestimate, or by doing an FFT on the long preamble correlator output.

B. Alternative Embodiment Using RSSI

Some embodiments (as described by reference to FIG. 17), measure time ofarrival, where correlation with the known preamble transmitted structureis used. FIG. 19 illustrates an alternative embodiment that uses thetime of arrival of the Received Signal Strength Indication (RSSI). Oneof ordinary skill in the art would realize that specific details of FIG.19 are given as an example and it is possible to use different circuitimplementations to achieve the same results without deviating from theteachings of the invention. Furthermore, the example of FIG. 19 uses thewireless LAN IEEE 802.11a standard to demonstrate methods of how toextract some of the channel's characteristics such as the time ofarrival delay using RSSI transition in some embodiments. However, one ofordinary skill in the art would realize that the disclosed methods canbe applied to any system that has a pre-amble structure such asBluetooth®, GPS, WLAN, cellular (2G, 3G, 4G), WiMAX, HD Radio™, UWB, and60 GHz standards. The proposed methodology works as long as both thetransmitter and receiver use the same standard.

FIG. 20 conceptually illustrates an example of an RSSI signal output ofsome embodiments. RSSI measures the power in the received RF signal. Thepower detection & filter module 1905 obtains the RSSI by squaring andsumming the downcoverted in-phase (I) and quadrature (Q) signalcomponents (see Markers I and Q in FIG. 19) and low pass filtering theresults. When the receiver 1715 receives a signal similar to the signalshown in FIG. 16, the RSSI signal (see Marker C in FIG. 19) is zerountil the first training symbol, t₁, arrives. The signal then increasesin value and stays up until the last data packet and then it transitionsback down to zero.

Some embodiments measure the time corresponding to the middle of theRSSI transition edge, when the signal spikes from a value close to zeroto its high power level. This time represents the time of arrival of thefirst short preamble training symbol, t₁, in FIG. 16. Since the RSSItransition time is calculated in firmware (performed by power detection& filter 1905, peak detector 1765, and stored in memory 1770) it ispossible to access it directly and use it instead of the preamblecorrelation peak delay.

The firmware also has its own clock for measuring time (same clock 1773that feeds the PLL 1725 and the ADC 1730). Therefore, using the RSSItransition time instead of the preamble correlation method has theadvantage that it does not require the hardware changes of registercounters for time measurements, since the function of the counter willbe emulated in firmware albeit at lower time resolution. However, theRSSI transition time is less accurate than the preamble correlation peakdelay in noisy environments. Also, when the peaks are close togetherthey would overlap. The short and long preamble correlator blocks arestill needed in order to determine the start time for the OFDM FFT.Other components of FIG. 19 are similar to those described by referenceto FIG. 17 above. One of ordinary skill in the art would realize thatspecific details of FIG. 19 are given as an example and it is possibleto use different circuit implementations to achieve the same resultswithout deviating from the teachings of the invention.

VII. Computer System

Many of the above-described processes and modules are implemented assoftware processes that are specified as a set of instructions recordedon a computer readable storage medium (also referred to as “computerreadable medium” or “machine readable medium”). These instructions areexecuted by one or more computational elements, such as one or moreprocessing units of one or more processors or other computationalelements like Application-Specific ICs (“ASIC”) and Field ProgrammableGate Arrays (“FPGA”). The execution of these instructions causes the setof computational elements to perform the actions indicated in theinstructions. Computer is meant in its broadest sense, and can includeany electronic device with a processor (e.g., moving scanner, mobiledevice, access point, etc.). Examples of computer readable mediainclude, but are not limited to, CD-ROMs, flash drives, RAM chips, harddrives, EPROMs, etc. The computer readable media does not includecarrier waves and/or electronic signals passing wirelessly or over wiredconnection.

In this specification, the term “software” includes firmware residing inread-only memory or applications stored in magnetic storage that can beread into memory for processing by one or more processors. Also, in someembodiments, multiple software inventions can be implemented as parts ofa larger program while remaining distinct software inventions. In someembodiments, multiple software inventions can also be implemented asseparate programs. Finally, any combination of separate programs thattogether implement a software invention described herein is within thescope of the invention. In some embodiments, the software programs wheninstalled to operate on one or more computer systems define one or morespecific machine implementations that execute and perform the operationsof the software programs.

FIG. 21 conceptually illustrates a computer system 2100 with which someembodiments of the invention are implemented. For example, the movingscanner described above by reference to FIG. 4, the mobile devicedescribed above by reference to FIG. 8, or the access points describedabove by reference to FIG. 10 may be at least partially implementedusing sets of instructions that are run on the computer system 2100. Asanother example, the processes described by reference to FIGS. 7, 9, 11,and 10 may be at least partially implemented using sets of instructionsthat are run on the computer system 2100.

Such a computer system includes various types of computer readablemediums and interfaces for various other types of computer readablemediums. Computer system 2100 includes a bus 2110, at least oneprocessing unit (e.g., a processor) 2120, a system memory 2130, aread-only memory (ROM) 2140, a permanent storage device 2150, inputdevices 2170, output devices 2180, and a network connection 2190. Thecomponents of the computer system 2100 are electronic devices thatautomatically perform operations based on digital and/or analog inputsignals. The various examples of user inputs described by reference toFIGS. 7 and 10 may be at least partially implemented using sets ofinstructions that are run on the computer system 2100 and displayedusing the output devices 2180.

One of ordinary skill in the art will recognize that the computer system2100 may be embodied in other specific forms without deviating from thespirit of the invention. For instance, the computer system may beimplemented using various specific devices either alone or incombination. For example, a local Personal Computer (PC) may include theinput devices 2170 and output devices 2180, while a remote PC mayinclude the other devices 2110-2150, with the local PC connected to theremote PC through a network that the local PC accesses through itsnetwork connection 2190 (where the remote PC is also connected to thenetwork through a network connection).

The bus 2110 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of thecomputer system 2100. In some cases, the bus 2110 may include wirelessand/or optical communication pathways in addition to or in place ofwired connections. For example, the input devices 2170 and/or outputdevices 2180 may be coupled to the system 2100 using a wireless localarea network (W-LAN) connection, Bluetooth®, or some other wirelessconnection protocol or system.

The bus 2110 communicatively connects, for example, the processor 2120with the system memory 2130, the ROM 2140, and the permanent storagedevice 2150. From these various memory units, the processor 2120retrieves instructions to execute and data to process in order toexecute the processes of some embodiments. In some embodiments theprocessor includes an FPGA, an ASIC, or various other electroniccomponents for execution instructions.

The ROM 2140 stores static data and instructions that are needed by theprocessor 2120 and other modules of the computer system. The permanentstorage device 2150, on the other hand, is a read-and-write memorydevice. This device is a non-volatile memory unit that storesinstructions and data even when the computer system 2100 is off. Someembodiments of the invention use a mass-storage device (such as amagnetic or optical disk and its corresponding disk drive) as thepermanent storage device 2150.

Other embodiments use a removable storage device (such as a floppy disk,flash drive, or CD-ROM) as the permanent storage device. Like thepermanent storage device 2150, the system memory 2130 is aread-and-write memory device. However, unlike storage device 2150, thesystem memory 2130 is a volatile read-and-write memory, such as a randomaccess memory (RAM). The system memory stores some of the instructionsand data that the processor needs at runtime. In some embodiments, thesets of instructions and/or data used to implement the invention'sprocesses are stored in the system memory 2130, the permanent storagedevice 2150, and/or the read-only memory 2140. For example, the variousmemory units include instructions for processing multimedia items inaccordance with some embodiments.

The bus 2110 also connects to the input devices 2170 and output devices2180. The input devices 2170 enable the user to communicate informationand select commands to the computer system. The input devices includealphanumeric keyboards and pointing devices (also called “cursor controldevices”). The input devices also include audio input devices (e.g.,microphones, MIDI musical instruments, etc.) and video input devices(e.g., video cameras, still cameras, optical scanning devices, etc.).The output devices 2180 include printers, electronic display devicesthat display still or moving images, and electronic audio devices thatplay audio generated by the computer system. For instance, these displaydevices may display a graphical user interface (GUI). The displaydevices include devices such as cathode ray tubes (“CRT”), liquidcrystal displays (“LCD”), plasma display panels (“PDP”),surface-conduction electron-emitter displays (alternatively referred toas a “surface electron display” or “SED”), etc. The audio devicesinclude a PC's sound card and speakers, a speaker on a cellular phone, aBluetooth® earpiece, etc. Some or all of these output devices may bewirelessly or optically connected to the computer system.

Finally, as shown in FIG. 21, bus 2110 also couples computer 2100 to anetwork 2190 through a network adapter (not shown). In this manner, thecomputer can be a part of a network of computers (such as a local areanetwork (“LAN”), a wide area network (“WAN”), an Intranet, or a networkof networks, such as the Internet. For example, the computer 2100 may becoupled to a web server (network 2190) so that a web browser executingon the computer 2100 can interact with the web server as a userinteracts with a GUI that operates in the web browser.

As mentioned above, some embodiments include electronic components, suchas microprocessors, storage and memory that store computer programinstructions in a machine-readable or computer-readable medium(alternatively referred to as computer-readable storage media,machine-readable media, or machine-readable storage media). Someexamples of such computer-readable media include RAM, ROM, read-onlycompact discs (CD-ROM), recordable compact discs (CD-R), rewritablecompact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM,dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g.,DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SDcards, micro-SD cards, etc.), magnetic and/or solid state hard drives,read-only and recordable blu-ray discs, ultra density optical discs, anyother optical or magnetic media, and floppy disks. The computer-readablemedia may store a computer program that is executable by a device suchas an electronics device, a microprocessor, a processor, amulti-processor (e.g., an IC with several processing units on it) andincludes sets of instructions for performing various operations. Thecomputer program excludes any wireless signals, wired download signals,and/or any other ephemeral signals.

Examples of hardware devices configured to store and execute sets ofinstructions include, but are not limited to, ASICs, FPGAs, programmablelogic devices (“PLDs”), ROM, and RAM devices. Examples of computerprograms or computer code include machine code, such as produced by acompiler, and files including higher-level code that are executed by acomputer, an electronic component, or a microprocessor using aninterpreter.

As used in this specification and any claims of this application, theterms “computer”, “computer system”, “server”, “processor”, and “memory”all refer to electronic or other technological devices. These termsexclude people or groups of people. For the purposes of thisspecification, the terms display or displaying mean displaying on anelectronic device. As used in this specification and any claims of thisapplication, the terms “computer readable medium”, “computer readablemedia”, “machine readable medium”, and “machine readable media” areentirely restricted to non-transitory, tangible, physical objects thatstore information in a form that is readable by a computer. These termsexclude any wireless signals, wired download signals, and/or any otherephemeral signals.

It should be recognized by one of ordinary skill in the art that any orall of the components of computer system 2100 may be used in conjunctionwith the invention. Moreover, one of ordinary skill in the art willappreciate that any other system configuration may also be used inconjunction with the invention or components of the invention.

While the invention has been described with reference to numerousspecific details, one of ordinary skill in the art will recognize thatthe invention can be embodied in other specific forms without departingfrom the spirit of the invention. Moreover, while the examples shownillustrate many individual modules as separate blocks, one of ordinaryskill in the art would recognize that some embodiments may combine thesemodules into a single functional block or element. One of ordinary skillin the art would also recognize that some embodiments may divide aparticular module into multiple modules. Furthermore, specific details(such as details shown in FIGS. 4, 6, 17, 19, etc.) are given as anexample and it is possible to use different circuit implementations toachieve the same results without deviating from the teachings of theinvention.

One of ordinary skill in the art would understand that the invention isnot to be limited by the foregoing illustrative details, but rather isto be defined by the appended claims.

1. A method of determining a location of a mobile device, the methodcomprising: receiving a first plurality of signals with knowntransmission patterns at the mobile device from a set of transmitters,the first plurality of signals comprising at least one multipath signal;computing a first set of parameters from the first plurality of signalsusing the known transmission pattern; retrieving, from a database, asecond set of parameters computed from a second plurality of signals,the second plurality of signals comprising at least one multipathsignal, each parameter associated with a location where a correspondingsignal from the second plurality of signals was received; and comparingthe first set of parameters with the second set of parameters; anddetermining the location of the mobile device based on the comparison.2. The method of claim 1, wherein a plurality of transmitters in the setof transmitters transmit over a particular transmission channel, whereinthe first and second sets of parameters comprise a plurality ofparameters characterizing the transmission channel, wherein the firstand second sets of parameters comprise channel multipath profile andchannel fast Fourier transform (FFT) coefficients.
 3. The method ofclaim 1, wherein the mobile device is moving, the method furthercomprising: calculating a velocity of the mobile device; and revisingthe determined position of the mobile device based on the velocity ofthe mobile device and a plurality of previously determined locations ofthe mobile device.
 4. The method of claim 3, wherein the parameters inthe first and second sets of parameters comprise a Doppler shift,wherein determining the location of the mobile device comprisesutilizing the Doppler shift.
 5. The method of claim 1, wherein the firstand second sets of parameters comprise time of arrival delay extractedusing received signal strength indicator (RSSI) transition, time ofarrival delay extracted using signal preamble correlation, timedifference of arrival, transmitter signal strength, angle of arrival,and equalizer filter coefficients.
 6. The method of claim 1, wherein atleast one signal comprises a set of pilot tones, wherein the first andsecond sets of parameters comprise phase and amplitude of each pilottone.
 7. The method of claim 1 further comprising: determining anestimated location of the mobile device prior to determining the mobilelocation; and limiting the second set of parameters to parametersassociated with locations in a vicinity of the estimated location of themobile device.
 8. The method of claim 7, wherein determining theestimated location of the mobile device comprises using a set ofpreviously determined locations of the mobile device.
 9. The method ofclaim 7, wherein each transmitter transmits an identification, whereineach parameter in the database is associated with an identification of atransmitter, wherein retrieving the second set of parameters comprisesretrieving parameters associated with identifications of thetransmitters transmitting the first plurality of signals.
 10. The methodof claim 1, wherein determining the location of the mobile device doesnot comprise using a location of a transmitter.
 11. The method of claim1, wherein determining the location of the mobile device does notcomprise determining a distance from the mobile device to a transmitter.12. The method of claim 1, wherein each location associated with aparameter in the second set of parameters is identified by a set ofcoordinate values, wherein determining the location of the mobile devicecomprises: for each particular location associated with a parameter inthe second set of parameters: multiplying each parameter in the firstset of parameters with each of a set of parameters in the second set ofparameters associated with the particular location; and summing a resultof each multiplication to determine a correlation parametercorresponding the particular location; normalizing each correlationcoefficient; and computing a set of coordinate values for the locationof the mobile device, each coordinate value for the location of themobile device computed as a weighed average of each normalizedcorrelation coefficient multiplied by a coordinate value in the set ofcoordinate values corresponding to the second set of parameters.
 13. Themethod of claim 1, wherein each signal has a set of preambles, whereincomputing the first set of parameters using the known transmissionpattern comprises determining a peak time of arrival for each signalbased on the set of signal preambles.
 14. The method of claim 13,wherein the mobile device and each transmitter operate using a clock,wherein the mobile device clock is not synchronized with the transmitterclocks, wherein the transmitters clocks are not synchronized with eachother, the method further comprising normalizing the peak time ofarrival of each signal to make the peak time of arrival of a firstsignal received at the mobile device to occur at a time equal to zero.15. The method of claim 1, wherein the first set of parameters comprisesa plurality of different parameter types, the method further comprising:calculating a location of the mobile device based on each parametertype; and determining the location of the mobile device as a weightedaverage of the calculated locations.
 16. The method of claim 15, whereinthe transmitters transmit signals for a plurality of radio types, eachradio type using a different standard, wherein the set of parameterscomprises a plurality of parameters computed from signals of one or moreof the radio types, the method further comprising: determining thelocation of the mobile device for each particular radio type as aweighted average of the calculated locations based on parameterscomputed from signals of the particular radio type; and determining thelocation of the mobile device as a weighted average of the locationsdetermined for each radio type.
 17. The method of claim 1, wherein thetransmitters transmit signals for a plurality of radio types, each radiotype using a different standard, the method further comprising:calculating a location of the mobile device based on parameters computedfrom signals of each radio type; and determining the location of themobile device as a weighted average of the calculated locations.
 18. Amethod of determining a location of a mobile device, the methodcomprising: at a set of scanning devices, receiving a plurality ofsignals from a plurality of signal sources, the plurality of signalscomprising at least one multipath signal; determining a location whereeach signal is received by a scanning device; computing a set ofparameters from the received signals; storing the set of computedparameters and the locations where each signal in a reference database;receiving a request for parameters to determine the location of themobile device; retrieving a set of parameters from the referencedatabase, the set of parameters for comparing with a set of parameterscomputed from signals received by the mobile device to determine thelocation of the mobile device.
 19. The method of claim 18, wherein a setof signal sources in the plurality of signal source move, wherein thesignals received from the moving signal sources vary in time with aknown period, the method further comprising: determining a time wheneach signal is received by a scanning device; storing the time eachsignal is received in the reference database; and retrieving the timeassociated with each signal along with the set of parameters retrievedfrom the database, wherein determining the location of mobile devicecomprises: determining a time when the mobile device received the set ofparameters; and using the time associated with each signal and the timewhen the mobile device received the set of parameters to revise thelocation of the mobile device.
 20. The method of claim 18, wherein theset of scanning devices comprises one or more moving devices, eachmoving device comprising a set of antennas for receiving signals. 21.The method of claim 20, wherein one or more of the scanning devices inthe set of scanning devices are position enabled, wherein a scanningdevice is position enabled when the scanning device comprises a set ofequipments for determining the scanning device's location.
 22. Themethod of claim 18, wherein one or more of the scanning devices in theset of scanning devices are position enabled portable wireless devices,wherein a wireless device is position enabled when the wireless devicecomprises a set of equipments for determining the wireless device'slocation.
 23. The method of claim 18, wherein one or more scanningdevices are fixed RF radios with known locations.
 24. The method ofclaim 18, wherein one or more scanning devices comprise more than oneantenna for receiving signals, wherein determining the location whereeach signal is received by a scanning device comprises determining alocation where each signal is received by each antenna of a scanningdevice.
 25. The method of claim 18, wherein one or more signal sourcesin the plurality of signal sources comprise a plurality of antennas fortransmitting signals.
 26. The method of claim 18 further comprising,updating the set of parameters in the reference database based on theparameters computed from the signals received by the wireless mobiledevice.
 27. A method of determining a location of a mobile device, themethod comprising: receiving a first plurality of signals at the mobiledevice from a transmitting device over a set of transmission channels,the transmitting device comprising a plurality of antennas, each of theplurality of signals received from one of the antennas of a transmittingdevice; characterizing the set of transmission channels to compute afirst plurality of parameters associated with the first plurality ofsignals; retrieving, from a database, a second plurality of parametersassociated with a plurality of signals previously received from thetransmitting device by a set of scanning devices; correlating the firstplurality of parameters with the second plurality of parameters; anddetermining the location of the mobile device based on the correlation.28. The method of claim 27, wherein characterizing the set oftransmission channels comprises characterizing a profile of the receivedsignals to determine a first plurality of peak time of arrivalsassociated with the profile of the signals received from the mobiledevice, wherein the second plurality of parameters comprise a secondplurality of peak time of arrivals associated with profiles of aplurality of signals previously received from the transmitting device bythe set of scanning devices.
 29. The method of claim 28, wherein themobile device and the transmitting device each operate using a clock,wherein the mobile device clock is not synchronized with thetransmitting device clock, the method further comprising normalizing thefirst plurality of peak time of arrivals to make the peak time ofarrival of a first received signal to occur at a time equal to zero. 30.The method of claim 27, wherein the first and second sets of parameterscomprise a plurality of parameters characterizing the set oftransmission channels, wherein the first and second sets of parameterscomprise multichannel profiles and channel fast Fourier transform (FFT)coefficients.
 31. The method of claim 27 further comprising receivingone or more multipath signals related to the first plurality of signals,wherein characterizing the profile of the received signals comprisescomputing a set of parameters for the multipath signals.
 32. The methodof claim 27, wherein the transmitting device transmits signals ofdifferent standards comprising at least two of Wireless Local AreaNetwork (WLAN) 802.11, Bluetooth, WiMax, HD Radio, Ultra-wideband (UWB),GPS, cellular, and 60 GHz standards.
 33. The method of claim 27 furthercomprising receiving signals from a plurality of transmitting devices,wherein the transmitting devices transmit signals of different standardscomprising at least two of Wireless Local Area Network (WLAN) 802.11,Bluetooth, WiMax, HD Radio, Ultra-wideband (UWB), GPS, cellular, and 60GHz standards.
 34. The method of claim 27, wherein the mobile devicecomprises a plurality of antennas for receiving signals, whereindetermining the location of the mobile device comprises: determining alocation of each of the plurality of mobile device antennas; anddetermining the location of the mobile device based on the locations ofthe plurality of mobile device antennas.
 35. The method of claim 27,wherein each of the plurality of transmitter antennas transmit with anassociated preset time delay in relation to other antennas, wherein themobile device receives signals from each particular transmitter antennaat a particular delay in relation to the signals received from the othertransmitter antennas, each particular delay proportional to (i) adistance between the particular antenna and (ii) the preset time delayassociated with the antenna.
 36. The method of claim 35, wherein the setof transmission channels comprises a plurality of channels, each channelassociated with one or more transmitter antenna.
 37. The method of claim27, wherein the plurality of antennas transmit together, wherein the setof transmission channels comprise one transmission channel for allantennas in the plurality of transmitter antennas.